'''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					,lowerCamelCase_					,lowerCamelCase_=13					,lowerCamelCase_=7					,lowerCamelCase_=True					,lowerCamelCase_=True					,lowerCamelCase_=True					,lowerCamelCase_=True					,lowerCamelCase_=99					,lowerCamelCase_=32					,lowerCamelCase_=5					,lowerCamelCase_=4					,lowerCamelCase_=4					,lowerCamelCase_="gelu"					,lowerCamelCase_=0.0					,lowerCamelCase_=0.1					,lowerCamelCase_=True					,lowerCamelCase_=512					,lowerCamelCase_=16					,lowerCamelCase_=2					,lowerCamelCase_=0.02					,lowerCamelCase_=3					,lowerCamelCase_=4					,lowerCamelCase_=None					,)			->			List[Any]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size							)

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

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

         UpperCAmelCase__							:				Dict   				=  self.get_config()

         return config, input_ids, input_mask, token_labels




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''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=lowerCamelCase_					,initializer_range=self.initializer_range					,)




      def 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  self.prepare_config_and_inputs()

         UpperCAmelCase__							:				List[Any]   				=  True

         return config, input_ids, input_mask, token_labels




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



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  GPTNeoXJapaneseModel(config=lowerCamelCase_							)
         model.to(lowerCamelCase_							)
         model.eval()
         UpperCAmelCase__							:				Optional[Any]   				=  model(lowerCamelCase_					,attention_mask=lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  model(lowerCamelCase_							)
         self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  GPTNeoXJapaneseForCausalLM(config=lowerCamelCase_							)
         model.to(lowerCamelCase_							)
         model.eval()
         UpperCAmelCase__							:				List[str]   				=  model(lowerCamelCase_					,attention_mask=lowerCamelCase_					,labels=lowerCamelCase_							)
         self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length, self.vocab_size)							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  True
         UpperCAmelCase__							:				Optional[int]   				=  GPTNeoXJapaneseForCausalLM(config=lowerCamelCase_							)
         model.to(lowerCamelCase_							)
         model.eval()

         # first forward pass
         UpperCAmelCase__							:				List[str]   				=  model(lowerCamelCase_					,attention_mask=lowerCamelCase_					,use_cache=lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         UpperCAmelCase__							:				Dict   				=  ids_tensor((self.batch_size, 3)					,config.vocab_size							)
         UpperCAmelCase__							:				Optional[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__							:				str   				=  torch.cat([input_mask, next_mask]					,dim=-1							)

         UpperCAmelCase__							:				Dict   				=  model(lowerCamelCase_					,attention_mask=lowerCamelCase_					,output_hidden_states=lowerCamelCase_							)
         UpperCAmelCase__							:				str   				=  output_from_no_past['''hidden_states'''][0]
         UpperCAmelCase__							:				Dict   				=  model(
             lowerCamelCase_					,attention_mask=lowerCamelCase_					,past_key_values=lowerCamelCase_					,output_hidden_states=lowerCamelCase_					,)['''hidden_states'''][0]

         # select random slice
         UpperCAmelCase__							:				Union[str, Any]   				=  ids_tensor((1,)					,output_from_past.shape[-1]							).item()
         UpperCAmelCase__							:				Tuple   				=  output_from_no_past[:, -3:, random_slice_idx].detach()
         UpperCAmelCase__							:				Optional[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(lowerCamelCase_					,lowerCamelCase_					,atol=1e-3							)							)




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				List[str]   				=  self.prepare_config_and_inputs()
         UpperCAmelCase__							:				Tuple   				=  config_and_inputs
         UpperCAmelCase__							:				Union[str, Any]   				=  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
         return config, inputs_dict





@require_torch
class 				_lowercase	(      lowerCAmelCase		,lowerCAmelCase		,unittest.TestCase   ):
      '''simple docstring'''


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




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



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  GPTNeoXJapaneseModelTester(self							)
         UpperCAmelCase__							:				List[str]   				=  ConfigTester(self					,config_class=lowerCamelCase_					,hidden_size=37							)




      def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



         '''simple docstring'''



         self.config_tester.run_common_tests()




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



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)




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



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)




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



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.model_tester.prepare_config_and_inputs_for_decoder()

         UpperCAmelCase__							:				int   				=  None

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




      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)




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



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase_							)




      @slow
      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  '''abeja/gpt-neox-japanese-2.7b'''

         UpperCAmelCase__							:				str   				=  ['''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[str]   				=  [
             '''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__							:				Tuple   				=  GPTNeoXJapaneseTokenizer.from_pretrained(lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  GPTNeoXJapaneseForCausalLM.from_pretrained(lowerCamelCase_							)

         UpperCAmelCase__							:				Any   				=  []
         for prompt in prompts:
            UpperCAmelCase__							:				List[Any]   				=  tokenizer(lowerCamelCase_					,return_tensors='''pt'''							).input_ids
            UpperCAmelCase__							:				List[str]   				=  model.generate(lowerCamelCase_					,max_length=50							)
            UpperCAmelCase__							:				Tuple   				=  tokenizer.batch_decode(lowerCamelCase_					,skip_special_tokens=lowerCamelCase_							)
            predicted_outputs += generated_string
         self.assertListEqual(lowerCamelCase_					,lowerCamelCase_							)





'''simple docstring'''

import unittest

import numpy as np







def __UpperCamelCase(   _A					:				np.ndarray  ,			_A					:				np.ndarray  ,			_A					:				np.ndarray  ,			_A					:				np.ndarray | None = None  ,			):
   '''simple docstring'''
   UpperCAmelCase__							:				Any   				=  np.shape(_A    )
   UpperCAmelCase__							:				List[Any]   				=  np.shape(_A    )
   UpperCAmelCase__							:				Tuple   				=  np.shape(_A    )

   if shape_a[0] != shape_b[0]:
      UpperCAmelCase__							:				Any   				=  (
          '''Expected the same number of rows for A and B. '''
          F'''Instead found A of size {shape_a} and B of size {shape_b}'''
      )
      raise ValueError(_A    )

   if shape_b[1] != shape_c[1]:
      UpperCAmelCase__							:				List[str]   				=  (
          '''Expected the same number of columns for B and C. '''
          F'''Instead found B of size {shape_b} and C of size {shape_c}'''
      )
      raise ValueError(_A    )

   UpperCAmelCase__							:				Optional[Any]   				=  pseudo_inv
   if a_inv is None:
      try:
         UpperCAmelCase__							:				str   				=  np.linalg.inv(_A    )
      except np.linalg.LinAlgError:
         raise ValueError(
             '''Input matrix A is not invertible. Cannot compute Schur complement.'''    )

   return mat_c - mat_b.T @ a_inv @ mat_b


class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				List[Any]   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Optional[Any]   				=  np.array([[2, 1], [6, 3]]							)

         UpperCAmelCase__							:				Tuple   				=  schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)

         UpperCAmelCase__							:				Tuple   				=  np.block([[a, b], [b.T, c]]							)

         UpperCAmelCase__							:				int   				=  np.linalg.det(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  np.linalg.det(lowerCamelCase_							)
         UpperCAmelCase__							:				Union[str, Any]   				=  np.linalg.det(lowerCamelCase_							)

         self.assertAlmostEqual(lowerCamelCase_					,det_a * det_s							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				Tuple   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Tuple   				=  np.array([[2, 1], [6, 3]]							)

         with self.assertRaises(lowerCamelCase_							):
            schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]							)
         UpperCAmelCase__							:				str   				=  np.array([[0, 3], [3, 0], [2, 3]]							)
         UpperCAmelCase__							:				Any   				=  np.array([[2, 1, 3], [6, 3, 5]]							)

         with self.assertRaises(lowerCamelCase_							):
            schur_complement(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  unittest.main()




