





"""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   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


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

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

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

        _UpperCamelCase   :						Optional[int]    =							None
        if self.use_labels:
              _UpperCamelCase   :						List[str]    =							ids_tensor([self.batch_size, self.seq_length]       , self.num_labels						)

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

        return config, input_ids, input_mask, token_labels

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Optional[Any]:
        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  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					int:
        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						List[str]    =							self.prepare_config_and_inputs()

        _UpperCamelCase   :						Any    =							True

        return config, input_ids, input_mask, token_labels

  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple       , __a :							str       , __a :							List[Any]       , __a :							List[str]						)		->					Tuple:
        _UpperCamelCase   :						Union[str, Any]    =							GPTNeoXJapaneseModel(config=__a						)
        model.to(__a						)
        model.eval()
        _UpperCamelCase   :						str    =							model(__a       , attention_mask=__a						)
        _UpperCamelCase   :						str    =							model(__a						)
        self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size)						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							Optional[int]       , __a :							Optional[int]       , __a :							Union[str, Any]						)		->					Any:
        _UpperCamelCase   :						Optional[int]    =							True
        _UpperCamelCase   :						Union[str, Any]    =							GPTNeoXJapaneseModel(__a						)
        model.to(__a						)
        model.eval()
        _UpperCamelCase   :						Any    =							model(__a       , attention_mask=__a						)
        self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size)						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]       , __a :							Optional[int]       , __a :							List[Any]       , __a :							Dict       , __a :							int						)		->					Optional[int]:
        _UpperCamelCase   :						Optional[int]    =							GPTNeoXJapaneseForCausalLM(config=__a						)
        model.to(__a						)
        model.eval()
        _UpperCamelCase   :						List[str]    =							model(__a       , attention_mask=__a       , labels=__a						)
        self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.vocab_size)						)

  def  __SCREAMING_SNAKE_CASE					(		self :							str       , __a :							Optional[Any]       , __a :							Union[str, Any]       , __a :							List[Any]						)		->					Tuple:
        _UpperCamelCase   :						int    =							True
        _UpperCamelCase   :						List[Any]    =							GPTNeoXJapaneseForCausalLM(config=__a						)
        model.to(__a						)
        model.eval()

        # first forward pass
        _UpperCamelCase   :						Optional[int]    =							model(__a       , attention_mask=__a       , use_cache=__a						)
        _UpperCamelCase   :						str    =							outputs.past_key_values

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

        _UpperCamelCase   :						str    =							model(__a       , attention_mask=__a       , output_hidden_states=__a						)
        _UpperCamelCase   :						Optional[int]    =							output_from_no_past["hidden_states"][0]
        _UpperCamelCase   :						Union[str, Any]    =							model(
            __a       , attention_mask=__a       , past_key_values=__a       , output_hidden_states=__a       , )["hidden_states"][0]

        # select random slice
        _UpperCamelCase   :						Tuple    =							ids_tensor((1,)       , output_from_past.shape[-1]						).item()
        _UpperCamelCase   :						int    =							output_from_no_past[:, -3:, random_slice_idx].detach()
        _UpperCamelCase   :						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(__a       , __a       , atol=1e-3						)						)


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





@require_torch
class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase		,	_UpperCamelCase		,	unittest.TestCase ):







  '''simple docstring'''


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

  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					Tuple:
        _UpperCamelCase   :						Optional[int]    =							GPTNeoXJapaneseModelTester(self						)
        _UpperCamelCase   :						Dict    =							ConfigTester(self       , config_class=__a       , hidden_size=37						)

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

  def  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					Optional[Any]:
        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Union[str, Any]    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(__a       , __a       , __a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					List[str]:
        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Tuple    =							self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(__a       , __a       , __a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					Optional[Any]:
        # This regression test was failing with PyTorch < 1.3
        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Optional[int]    =							self.model_tester.prepare_config_and_inputs_for_decoder()

        _UpperCamelCase   :						Union[str, Any]    =							None

        self.model_tester.create_and_check_model_as_decoder(__a       , __a       , __a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					Tuple:
        _UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase,	_UpperCamelCase   :						Dict    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(__a       , __a       , __a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Optional[int]:
        _UpperCamelCase   :						Tuple    =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_causal_lm(*__a						)


  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							List[Any]						)		->					Tuple:
        _UpperCamelCase   :						Optional[Any]    =							"abeja/gpt-neox-japanese-2.7b"

        _UpperCamelCase   :						Dict    =							["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   :						Optional[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   :						Any    =							GPTNeoXJapaneseTokenizer.from_pretrained(__a						)
        _UpperCamelCase   :						Optional[Any]    =							GPTNeoXJapaneseForCausalLM.from_pretrained(__a						)

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







"""simple docstring"""


import numpy as np




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_ = 1e-12		,lowercase_ = 100		,)       ->		tuple[float, np.ndarray]:




      """simple docstring"""

      assert np.shape(lowercase_      )[0] == np.shape(lowercase_      )[1]
      # Ensure proper dimensionality.
      assert np.shape(lowercase_      )[0] == np.shape(lowercase_      )[0]
      # Ensure inputs are either both complex or both real
      assert np.iscomplexobj(lowercase_      ) == np.iscomplexobj(lowercase_      )
      _UpperCamelCase   :						Dict    =							np.iscomplexobj(lowercase_      )
      if is_complex:
            # Ensure complex input_matrix is Hermitian
            assert np.array_equal(lowercase_		,input_matrix.conj().T      )

      # Set convergence to False. Will define convergence when we exceed max_iterations
      # or when we have small changes from one iteration to next.

      _UpperCamelCase   :						List[Any]    =							False
      _UpperCamelCase   :						Optional[int]    =							0
      _UpperCamelCase   :						int    =							0
      _UpperCamelCase   :						Optional[int]    =							1e12

      while not convergence:
            # Multiple matrix by the vector.
            _UpperCamelCase   :						Optional[int]    =							np.dot(lowercase_		,lowercase_      )
            # Normalize the resulting output vector.
            _UpperCamelCase   :						List[Any]    =							w / np.linalg.norm(lowercase_      )
            # Find rayleigh quotient
            # (faster than usual b/c we know vector is normalized already)
            _UpperCamelCase   :						Optional[Any]    =							vector.conj().T if is_complex else vector.T
            _UpperCamelCase   :						Dict    =							np.dot(lowercase_		,np.dot(lowercase_		,lowercase_      )      )

            # Check convergence.
            _UpperCamelCase   :						Optional[int]    =							np.abs(lambda_ - lambda_previous      ) / lambda_
            iterations += 1

            if error <= error_tol or iterations >= max_iterations:
                  _UpperCamelCase   :						List[Any]    =							True

            _UpperCamelCase   :						Tuple    =							lambda_

      if is_complex:
            _UpperCamelCase   :						Tuple    =							np.real(lambda_      )

      return lambda_, vector




def        lowercase__     (       )       ->		None:




      """simple docstring"""

      _UpperCamelCase   :						Dict    =							np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]      )
      _UpperCamelCase   :						Optional[int]    =							np.array([41, 4, 20]      )
      _UpperCamelCase   :						Union[str, Any]    =							real_input_matrix.astype(np.complexaaa      )
      _UpperCamelCase   :						int    =							np.triu(1J * complex_input_matrix		,1      )
      complex_input_matrix += imag_matrix
      complex_input_matrix += -1 * imag_matrix.T
      _UpperCamelCase   :						Union[str, Any]    =							np.array([41, 4, 20]      ).astype(np.complexaaa      )

      for problem_type in ["real", "complex"]:
            if problem_type == "real":
                  _UpperCamelCase   :						Dict    =							real_input_matrix
                  _UpperCamelCase   :						Any    =							real_vector
            elif problem_type == "complex":
                  _UpperCamelCase   :						int    =							complex_input_matrix
                  _UpperCamelCase   :						Union[str, Any]    =							complex_vector

            # Our implementation.
            _UpperCamelCase,	_UpperCamelCase   :						int    =							power_iteration(lowercase_		,lowercase_      )

            # Numpy implementation.

            # Get eigenvalues and eigenvectors using built-in numpy
            # eigh (eigh used for symmetric or hermetian matrices).
            _UpperCamelCase,	_UpperCamelCase   :						str    =							np.linalg.eigh(lowercase_      )
            # Last eigenvalue is the maximum one.
            _UpperCamelCase   :						Any    =							eigen_values[-1]
            # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
            _UpperCamelCase   :						Tuple    =							eigen_vectors[:, -1]

            # Check our implementation and numpy gives close answers.
            assert np.abs(eigen_value - eigen_value_max      ) <= 1e-6
            # Take absolute values element wise of each eigenvector.
            # as they are only unique to a minus sign.
            assert np.linalg.norm(np.abs(lowercase_      ) - np.abs(lowercase_      )      ) <= 1e-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()
