




import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase=0.999,	lowerCamelCase="cosine",	):
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowerCamelCase      ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(lowerCamelCase      ):
      return math.exp(t * -12.0      )

  else:
    raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"      )

  lowercase			:List[str]							=  []
  for i in range(lowerCamelCase      ):
    lowercase			:Tuple							=  i / num_diffusion_timesteps
    lowercase			:Optional[Any]							=  (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(lowerCamelCase      ) / alpha_bar_fn(lowerCamelCase      ),	lowerCamelCase      )      )
  return torch.tensor(lowerCamelCase,	dtype=torch.floataa      )







class       __lowerCAmelCase				(							lowerCAmelCase					,       lowerCAmelCase):
       _a           =  [e.name for e in KarrasDiffusionSchedulers]
       _a           =  2






       @register_to_config
       def __init__(      self:    Any				,    _lowerCAmelCase:    int = 10_00				,    _lowerCAmelCase:    float = 0.0_00_85				,    _lowerCAmelCase:    float = 0.0_12				,    _lowerCAmelCase:    str = "linear"				,    _lowerCAmelCase:    Optional[Union[np.ndarray, List[float]]] = None				,    _lowerCAmelCase:    str = "epsilon"				,    _lowerCAmelCase:    Optional[bool] = False				,    _lowerCAmelCase:    Optional[bool] = False				,    _lowerCAmelCase:    float = 1.0				,    _lowerCAmelCase:    str = "linspace"				,    _lowerCAmelCase:    int = 0				,    ):
         if trained_betas is not None:
           lowercase			:str							=  torch.tensor(_lowerCAmelCase				,    dtype=torch.floataa )
         elif beta_schedule == "linear":
           lowercase			:Optional[Any]							=  torch.linspace(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    dtype=torch.floataa )
         elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           lowercase			:List[Any]							=  (
               torch.linspace(beta_start**0.5				,    beta_end**0.5				,    _lowerCAmelCase				,    dtype=torch.floataa ) ** 2
           )
         elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           lowercase			:Optional[int]							=  betas_for_alpha_bar(_lowerCAmelCase				,    alpha_transform_type="cosine" )
         elif beta_schedule == "exp":
           lowercase			:Tuple							=  betas_for_alpha_bar(_lowerCAmelCase				,    alpha_transform_type="exp" )
         else:
           raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}" )

         lowercase			:List[Any]							=  1.0 - self.betas
         lowercase			:Union[str, Any]							=  torch.cumprod(self.alphas				,    dim=0 )

         #  set all values
         self.set_timesteps(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )
         lowercase			:Any							=  use_karras_sigmas






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any]				,    _lowerCAmelCase:    List[str]				,    _lowerCAmelCase:    Optional[Any]=None ):
         if schedule_timesteps is None:
           lowercase			:Tuple							=  self.timesteps

         lowercase			:Any							=  (schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter ) == 0:
           lowercase			:Tuple							=  1 if len(_lowerCAmelCase ) > 1 else 0
         else:
           lowercase			:str							=  timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase ) else timestep
           lowercase			:Union[str, Any]							=  self._index_counter[timestep_int]

         return indices[pos].item()






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
           return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    torch.FloatTensor				,    _lowerCAmelCase:    Union[float, torch.FloatTensor]				,    ):
         lowercase			:Optional[Any]							=  self.index_for_timestep(_lowerCAmelCase )

         lowercase			:Optional[Any]							=  self.sigmas[step_index]
         lowercase			:str							=  sample / ((sigma**2 + 1) ** 0.5)
         return sample






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    Union[str, torch.device] = None				,    _lowerCAmelCase:    Optional[int] = None				,    ):
         lowercase			:List[str]							=  num_inference_steps

         lowercase			:Optional[int]							=  num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
           lowercase			:int							=  np.linspace(0				,    num_train_timesteps - 1				,    _lowerCAmelCase				,    dtype=_lowerCAmelCase )[::-1].copy()
         elif self.config.timestep_spacing == "leading":
           lowercase			:List[str]							=  num_train_timesteps // self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           lowercase			:List[str]							=  (np.arange(0				,    _lowerCAmelCase ) * step_ratio).round()[::-1].copy().astype(_lowerCAmelCase )
           timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
           lowercase			:List[str]							=  num_train_timesteps / self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           lowercase			:List[str]							=  (np.arange(_lowerCAmelCase				,    0				,    -step_ratio )).round().copy().astype(_lowerCAmelCase )
           timesteps -= 1
         else:
           raise ValueError(
               F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'." )

         lowercase			:str							=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
         lowercase			:int							=  np.log(_lowerCAmelCase )
         lowercase			:Optional[Any]							=  np.interp(_lowerCAmelCase				,    np.arange(0				,    len(_lowerCAmelCase ) )				,    _lowerCAmelCase )

         if self.config.use_karras_sigmas:
           lowercase			:str							=  self._convert_to_karras(in_sigmas=_lowerCAmelCase				,    num_inference_steps=self.num_inference_steps )
           lowercase			:Any							=  np.array([self._sigma_to_t(_lowerCAmelCase				,    _lowerCAmelCase ) for sigma in sigmas] )

         lowercase			:str							=  np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
         lowercase			:Optional[int]							=  torch.from_numpy(_lowerCAmelCase ).to(device=_lowerCAmelCase )
         lowercase			:Union[str, Any]							=  torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )

         lowercase			:Optional[int]							=  torch.from_numpy(_lowerCAmelCase )
         lowercase			:str							=  torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )

         if str(_lowerCAmelCase ).startswith("mps" ):
           # mps does not support float64
           lowercase			:Tuple							=  timesteps.to(_lowerCAmelCase				,    dtype=torch.floataa )
         else:
           lowercase			:Tuple							=  timesteps.to(device=_lowerCAmelCase )

         # empty dt and derivative
         lowercase			:List[Any]							=  None
         lowercase			:str							=  None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         lowercase			:Optional[int]							=  defaultdict(_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    Tuple ):
         # get log sigma
         lowercase			:Optional[int]							=  np.log(_lowerCAmelCase )

         # get distribution
         lowercase			:List[str]							=  log_sigma - log_sigmas[:, np.newaxis]

         # get sigmas range
         lowercase			:Dict							=  np.cumsum((dists >= 0)				,    axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
         lowercase			:Optional[Any]							=  low_idx + 1

         lowercase			:Optional[int]							=  log_sigmas[low_idx]
         lowercase			:int							=  log_sigmas[high_idx]

         # interpolate sigmas
         lowercase			:str							=  (low - log_sigma) / (low - high)
         lowercase			:Union[str, Any]							=  np.clip(_lowerCAmelCase				,    0				,    1 )

         # transform interpolation to time range
         lowercase			:Optional[Any]							=  (1 - w) * low_idx + w * high_idx
         lowercase			:List[Any]							=  t.reshape(sigma.shape )
         return t






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    torch.FloatTensor				,    _lowerCAmelCase:    Union[str, Any] ):
         lowercase			:float							=  in_sigmas[-1].item()
         lowercase			:float							=  in_sigmas[0].item()

         lowercase			:str							=  7.0  # 7.0 is the value used in the paper
         lowercase			:Any							=  np.linspace(0				,    1				,    _lowerCAmelCase )
         lowercase			:str							=  sigma_min ** (1 / rho)
         lowercase			:Optional[Any]							=  sigma_max ** (1 / rho)
         lowercase			:Optional[Any]							=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         return self.dt is None






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[Any]				,    _lowerCAmelCase:    Union[torch.FloatTensor, np.ndarray]				,    _lowerCAmelCase:    Union[float, torch.FloatTensor]				,    _lowerCAmelCase:    Union[torch.FloatTensor, np.ndarray]				,    _lowerCAmelCase:    bool = True				,    ):
         lowercase			:str							=  self.index_for_timestep(_lowerCAmelCase )

         # advance index counter by 1
         lowercase			:int							=  timestep.cpu().item() if torch.is_tensor(_lowerCAmelCase ) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
           lowercase			:Dict							=  self.sigmas[step_index]
           lowercase			:Dict							=  self.sigmas[step_index + 1]
         else:
           # 2nd order / Heun's method
           lowercase			:List[str]							=  self.sigmas[step_index - 1]
           lowercase			:List[str]							=  self.sigmas[step_index]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         lowercase			:Any							=  0
         lowercase			:Optional[int]							=  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
           lowercase			:Any							=  sigma_hat if self.state_in_first_order else sigma_next
           lowercase			:Union[str, Any]							=  sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
           lowercase			:List[Any]							=  sigma_hat if self.state_in_first_order else sigma_next
           lowercase			:str							=  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
               sample / (sigma_input**2 + 1)
           )
         elif self.config.prediction_type == "sample":
           lowercase			:Dict							=  model_output
         else:
           raise ValueError(
               F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`" )

         if self.config.clip_sample:
           lowercase			:List[Any]							=  pred_original_sample.clamp(
               -self.config.clip_sample_range				,    self.config.clip_sample_range )

         if self.state_in_first_order:
           # 2. Convert to an ODE derivative for 1st order
           lowercase			:List[Any]							=  (sample - pred_original_sample) / sigma_hat
           # 3. delta timestep
           lowercase			:Dict							=  sigma_next - sigma_hat

           # store for 2nd order step
           lowercase			:Dict							=  derivative
           lowercase			:Optional[Any]							=  dt
           lowercase			:Union[str, Any]							=  sample
         else:
           # 2. 2nd order / Heun's method
           lowercase			:Optional[Any]							=  (sample - pred_original_sample) / sigma_next
           lowercase			:str							=  (self.prev_derivative + derivative) / 2

           # 3. take prev timestep & sample
           lowercase			:List[Any]							=  self.dt
           lowercase			:Any							=  self.sample

           # free dt and derivative
           # Note, this puts the scheduler in "first order mode"
           lowercase			:List[Any]							=  None
           lowercase			:str							=  None
           lowercase			:int							=  None

         lowercase			:Optional[Any]							=  sample + derivative * dt

         if not return_dict:
           return (prev_sample,)

         return SchedulerOutput(prev_sample=_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any]				,    _lowerCAmelCase:    torch.FloatTensor				,    _lowerCAmelCase:    torch.FloatTensor				,    _lowerCAmelCase:    torch.FloatTensor				,    ):
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         lowercase			:List[Any]							=  self.sigmas.to(device=original_samples.device				,    dtype=original_samples.dtype )
         if original_samples.device.type == "mps" and torch.is_floating_point(_lowerCAmelCase ):
           # mps does not support float64
           lowercase			:List[str]							=  self.timesteps.to(original_samples.device				,    dtype=torch.floataa )
           lowercase			:List[str]							=  timesteps.to(original_samples.device				,    dtype=torch.floataa )
         else:
           lowercase			:Dict							=  self.timesteps.to(original_samples.device )
           lowercase			:Tuple							=  timesteps.to(original_samples.device )

         lowercase			:int							=  [self.index_for_timestep(_lowerCAmelCase				,    _lowerCAmelCase ) for t in timesteps]

         lowercase			:Dict							=  sigmas[step_indices].flatten()
         while len(sigma.shape ) < len(original_samples.shape ):
           lowercase			:Union[str, Any]							=  sigma.unsqueeze(-1 )

         lowercase			:Union[str, Any]							=  original_samples + noise * sigma
         return noisy_samples






       def __len__(      self:    Tuple ):
         return self.config.num_train_timesteps









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				:






       def __init__(      self:    List[str]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    List[str]=13				,    _lowerCAmelCase:    Dict=7				,    _lowerCAmelCase:    Tuple=True				,    _lowerCAmelCase:    Dict=True				,    _lowerCAmelCase:    Optional[Any]=True				,    _lowerCAmelCase:    Union[str, Any]=True				,    _lowerCAmelCase:    str=99				,    _lowerCAmelCase:    Dict=32				,    _lowerCAmelCase:    Dict=5				,    _lowerCAmelCase:    int=4				,    _lowerCAmelCase:    Optional[Any]=4				,    _lowerCAmelCase:    Any="gelu"				,    _lowerCAmelCase:    Any=0.0				,    _lowerCAmelCase:    List[Any]=0.1				,    _lowerCAmelCase:    Optional[int]=True				,    _lowerCAmelCase:    Any=5_12				,    _lowerCAmelCase:    Optional[Any]=16				,    _lowerCAmelCase:    str=2				,    _lowerCAmelCase:    Tuple=0.02				,    _lowerCAmelCase:    str=3				,    _lowerCAmelCase:    List[str]=4				,    _lowerCAmelCase:    Optional[Any]=None				,    ):
         lowercase			:List[str]							=  parent
         lowercase			:List[str]							=  batch_size
         lowercase			:List[str]							=  seq_length
         lowercase			:Dict							=  is_training
         lowercase			:Union[str, Any]							=  use_input_mask
         lowercase			:Optional[int]							=  use_token_type_ids
         lowercase			:Dict							=  use_labels
         lowercase			:Any							=  vocab_size
         lowercase			:List[Any]							=  hidden_size
         lowercase			:Optional[int]							=  num_hidden_layers
         lowercase			:Union[str, Any]							=  num_attention_heads
         lowercase			:Any							=  intermediate_multiple_size
         lowercase			:str							=  hidden_act
         lowercase			:List[Any]							=  hidden_dropout
         lowercase			:Optional[Any]							=  attention_dropout
         lowercase			:int							=  weight_tying
         lowercase			:Tuple							=  max_position_embeddings
         lowercase			:str							=  type_vocab_size
         lowercase			:Union[str, Any]							=  type_sequence_label_size
         lowercase			:Dict							=  initializer_range
         lowercase			:Dict							=  num_labels
         lowercase			:Dict							=  num_choices
         lowercase			:Any							=  scope






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:List[Any]							=  ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size )

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

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

         lowercase			:Tuple							=  self.get_config()

         return config, input_ids, input_mask, token_labels






       def     SCREAMING_SNAKE_CASE			(      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     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Any							=  self.prepare_config_and_inputs()

         lowercase			:Tuple							=  True

         return config, input_ids, input_mask, token_labels






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    Optional[Any]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    List[str] ):
         lowercase			:List[str]							=  GPTNeoXJapaneseModel(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:Optional[int]							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase )
         lowercase			:Dict							=  model(_lowerCAmelCase )
         self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    List[Any]				,    _lowerCAmelCase:    int ):
         lowercase			:Dict							=  True
         lowercase			:Optional[Any]							=  GPTNeoXJapaneseModel(_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:List[str]							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase )
         self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    int				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    List[Any] ):
         lowercase			:str							=  GPTNeoXJapaneseForCausalLM(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()
         lowercase			:Tuple							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    labels=_lowerCAmelCase )
         self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size) )






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    _lowerCAmelCase:    List[Any]				,    _lowerCAmelCase:    int				,    _lowerCAmelCase:    Any ):
         lowercase			:Optional[Any]							=  True
         lowercase			:List[str]							=  GPTNeoXJapaneseForCausalLM(config=_lowerCAmelCase )
         model.to(_lowerCAmelCase )
         model.eval()

         # first forward pass
         lowercase			:str							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    use_cache=_lowerCAmelCase )
         lowercase			:Tuple							=  outputs.past_key_values

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

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

         lowercase			:Dict							=  model(_lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    output_hidden_states=_lowerCAmelCase )
         lowercase			:List[str]							=  output_from_no_past["hidden_states"][0]
         lowercase			:str							=  model(
             _lowerCAmelCase				,    attention_mask=_lowerCAmelCase				,    past_key_values=_lowerCAmelCase				,    output_hidden_states=_lowerCAmelCase				,    )["hidden_states"][0]

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






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase			:str							=  self.prepare_config_and_inputs()
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  config_and_inputs
         lowercase			:Union[str, Any]							=  {"input_ids": input_ids, "attention_mask": input_mask}
         return config, inputs_dict







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






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         lowercase			:Any							=  GPTNeoXJapaneseModelTester(self )
         lowercase			:List[str]							=  ConfigTester(self				,    config_class=_lowerCAmelCase				,    hidden_size=37 )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         self.config_tester.run_common_tests()






       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Optional[Any]							=  self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         # This regression test was failing with PyTorch < 1.3
         lowercase		,			lowercase		,			lowercase		,			lowercase			:Tuple							=  self.model_tester.prepare_config_and_inputs_for_decoder()

         lowercase			:Union[str, Any]							=  None

         self.model_tester.create_and_check_model_as_decoder(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         lowercase		,			lowercase		,			lowercase		,			lowercase			:List[str]							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(_lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:Optional[int]							=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*_lowerCAmelCase )






       @slow
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         lowercase			:int							=  "abeja/gpt-neox-japanese-2.7b"

         lowercase			: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、"]

         lowercase			:int							=  [
             "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。",
         ]

         lowercase			:List[str]							=  GPTNeoXJapaneseTokenizer.from_pretrained(_lowerCAmelCase )
         lowercase			:Tuple							=  GPTNeoXJapaneseForCausalLM.from_pretrained(_lowerCAmelCase )

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



