
"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


__magic_name__       =			logging.get_logger(__name__)



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		if isinstance(UpperCamelCase_  ,				(list, tuple)				) and isinstance(videos[0]  ,				(list, tuple)				) and is_valid_image(videos[0][0]				):
				return videos

		elif isinstance(UpperCamelCase_  ,				(list, tuple)				) and is_valid_image(videos[0]				):
				return [videos]

		elif is_valid_image(UpperCamelCase_				):
				return [[videos]]

		raise ValueError(f"Could not make batched video from {videos}"				)
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  List[str]						   =		['''pixel_values''']




		def __init__(     self							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = PILImageResampling.BILINEAR							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = 1 / 2_5_5							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				super().__init__(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = size if size is not None else {"""shortest_edge""": 2_2_4}
				__SCREAMING_SNAKE_CASE            = get_size_dict(lowerCAmelCase__							,  default_to_square=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
				__SCREAMING_SNAKE_CASE            = get_size_dict(lowerCAmelCase__							,  param_name="""crop_size""")

				__SCREAMING_SNAKE_CASE            = do_resize
				__SCREAMING_SNAKE_CASE            = size
				__SCREAMING_SNAKE_CASE            = do_center_crop
				__SCREAMING_SNAKE_CASE            = crop_size
				__SCREAMING_SNAKE_CASE            = resample
				__SCREAMING_SNAKE_CASE            = do_rescale
				__SCREAMING_SNAKE_CASE            = rescale_factor
				__SCREAMING_SNAKE_CASE            = do_normalize
				__SCREAMING_SNAKE_CASE            = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				__SCREAMING_SNAKE_CASE            = image_std if image_std is not None else IMAGENET_STANDARD_STD




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__ = PILImageResampling.BILINEAR							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = get_size_dict(lowerCAmelCase__							,  default_to_square=lowerCAmelCase__)
				if "shortest_edge" in size:
						__SCREAMING_SNAKE_CASE            = get_resize_output_image_size(lowerCAmelCase__							,  size["""shortest_edge"""]							,  default_to_square=lowerCAmelCase__)
				elif "height" in size and "width" in size:
						__SCREAMING_SNAKE_CASE            = (size["""height"""], size["""width"""])
				else:
						raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
				return resize(lowerCAmelCase__							,  size=lowerCAmelCase__							,  resample=lowerCAmelCase__							,  data_format=lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = get_size_dict(lowerCAmelCase__)
				if "height" not in size or "width" not in size:
						raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}")
				return center_crop(lowerCAmelCase__							,  size=(size["""height"""], size["""width"""])							,  data_format=lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				return rescale(lowerCAmelCase__							,  scale=lowerCAmelCase__							,  data_format=lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				return normalize(lowerCAmelCase__							,  mean=lowerCAmelCase__							,  std=lowerCAmelCase__							,  data_format=lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = ChannelDimension.FIRST							,  ):
				if do_resize and size is None or resample is None:
						raise ValueError("""Size and resample must be specified if do_resize is True.""")

				if do_center_crop and crop_size is None:
						raise ValueError("""Crop size must be specified if do_center_crop is True.""")

				if do_rescale and rescale_factor is None:
						raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

				if do_normalize and (image_mean is None or image_std is None):
						raise ValueError("""Image mean and std must be specified if do_normalize is True.""")

				# All transformations expect numpy arrays.
				__SCREAMING_SNAKE_CASE            = to_numpy_array(lowerCAmelCase__)

				if do_resize:
						__SCREAMING_SNAKE_CASE            = self.resize(image=lowerCAmelCase__							,  size=lowerCAmelCase__							,  resample=lowerCAmelCase__)

				if do_center_crop:
						__SCREAMING_SNAKE_CASE            = self.center_crop(lowerCAmelCase__							,  size=lowerCAmelCase__)

				if do_rescale:
						__SCREAMING_SNAKE_CASE            = self.rescale(image=lowerCAmelCase__							,  scale=lowerCAmelCase__)

				if do_normalize:
						__SCREAMING_SNAKE_CASE            = self.normalize(image=lowerCAmelCase__							,  mean=lowerCAmelCase__							,  std=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = to_channel_dimension_format(lowerCAmelCase__							,  lowerCAmelCase__)
				return image




		def 	snake_case_     (     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = ChannelDimension.FIRST							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = do_resize if do_resize is not None else self.do_resize
				__SCREAMING_SNAKE_CASE            = resample if resample is not None else self.resample
				__SCREAMING_SNAKE_CASE            = do_center_crop if do_center_crop is not None else self.do_center_crop
				__SCREAMING_SNAKE_CASE            = do_rescale if do_rescale is not None else self.do_rescale
				__SCREAMING_SNAKE_CASE            = rescale_factor if rescale_factor is not None else self.rescale_factor
				__SCREAMING_SNAKE_CASE            = do_normalize if do_normalize is not None else self.do_normalize
				__SCREAMING_SNAKE_CASE            = image_mean if image_mean is not None else self.image_mean
				__SCREAMING_SNAKE_CASE            = image_std if image_std is not None else self.image_std

				__SCREAMING_SNAKE_CASE            = size if size is not None else self.size
				__SCREAMING_SNAKE_CASE            = get_size_dict(lowerCAmelCase__							,  default_to_square=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = crop_size if crop_size is not None else self.crop_size
				__SCREAMING_SNAKE_CASE            = get_size_dict(lowerCAmelCase__							,  param_name="""crop_size""")

				if not valid_images(lowerCAmelCase__):
						raise ValueError(
						    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
						    """torch.Tensor, tf.Tensor or jax.ndarray.""")

				__SCREAMING_SNAKE_CASE            = make_batched(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = [
				    [
				        self._preprocess_image(
				            image=lowerCAmelCase__							,  do_resize=lowerCAmelCase__							,  size=lowerCAmelCase__							,  resample=lowerCAmelCase__							,  do_center_crop=lowerCAmelCase__							,  crop_size=lowerCAmelCase__							,  do_rescale=lowerCAmelCase__							,  rescale_factor=lowerCAmelCase__							,  do_normalize=lowerCAmelCase__							,  image_mean=lowerCAmelCase__							,  image_std=lowerCAmelCase__							,  data_format=lowerCAmelCase__							,  )
				        for img in video
				    ]
				    for video in videos
				]

				__SCREAMING_SNAKE_CASE            = {"""pixel_values""": videos}
				return BatchFeature(data=lowerCAmelCase__							,  tensor_type=lowerCAmelCase__)





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 __snake_case				:




   def __init__(    self			:				Optional[int]       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				List[Any]=13       ,	_lowercase			:				List[Any]=7       ,	_lowercase			:				Optional[int]=True       ,	_lowercase			:				str=True       ,	_lowercase			:				str=True       ,	_lowercase			:				List[str]=True       ,	_lowercase			:				List[str]=99       ,	_lowercase			:				List[str]=32       ,	_lowercase			:				str=5       ,	_lowercase			:				str=4       ,	_lowercase			:				str=4       ,	_lowercase			:				Union[str, Any]="gelu"       ,	_lowercase			:				str=0.0       ,	_lowercase			:				Union[str, Any]=0.1       ,	_lowercase			:				List[str]=True       ,	_lowercase			:				Union[str, Any]=5_12       ,	_lowercase			:				List[str]=16       ,	_lowercase			:				Dict=2       ,	_lowercase			:				int=0.02       ,	_lowercase			:				Any=3       ,	_lowercase			:				int=4       ,	_lowercase			:				List[str]=None       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      parent
     SCREAMING_SNAKE_CASE__										=      batch_size
     SCREAMING_SNAKE_CASE__										=      seq_length
     SCREAMING_SNAKE_CASE__										=      is_training
     SCREAMING_SNAKE_CASE__										=      use_input_mask
     SCREAMING_SNAKE_CASE__										=      use_token_type_ids
     SCREAMING_SNAKE_CASE__										=      use_labels
     SCREAMING_SNAKE_CASE__										=      vocab_size
     SCREAMING_SNAKE_CASE__										=      hidden_size
     SCREAMING_SNAKE_CASE__										=      num_hidden_layers
     SCREAMING_SNAKE_CASE__										=      num_attention_heads
     SCREAMING_SNAKE_CASE__										=      intermediate_multiple_size
     SCREAMING_SNAKE_CASE__										=      hidden_act
     SCREAMING_SNAKE_CASE__										=      hidden_dropout
     SCREAMING_SNAKE_CASE__										=      attention_dropout
     SCREAMING_SNAKE_CASE__										=      weight_tying
     SCREAMING_SNAKE_CASE__										=      max_position_embeddings
     SCREAMING_SNAKE_CASE__										=      type_vocab_size
     SCREAMING_SNAKE_CASE__										=      type_sequence_label_size
     SCREAMING_SNAKE_CASE__										=      initializer_range
     SCREAMING_SNAKE_CASE__										=      num_labels
     SCREAMING_SNAKE_CASE__										=      num_choices
     SCREAMING_SNAKE_CASE__										=      scope




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size      )

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

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

     SCREAMING_SNAKE_CASE__										=      self.get_config()

     return config, input_ids, input_mask, token_labels




   def 	__a						(    self			:				Optional[int]      ):



     """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=_lowercase       ,	initializer_range=self.initializer_range       ,	)




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.prepare_config_and_inputs()

     SCREAMING_SNAKE_CASE__										=      True

     return config, input_ids, input_mask, token_labels




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				int       ,	_lowercase			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      GPTNeoXJapaneseModel(config=_lowercase      )
     model.to(_lowercase      )
     model.eval()
     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      model(_lowercase      )
     self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)      )




   def 	__a						(    self			:				Dict       ,	_lowercase			:				int       ,	_lowercase			:				str       ,	_lowercase			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      True
     SCREAMING_SNAKE_CASE__										=      GPTNeoXJapaneseModel(_lowercase      )
     model.to(_lowercase      )
     model.eval()
     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase      )
     self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.seq_length, self.hidden_size)      )




   def 	__a						(    self			:				Any       ,	_lowercase			:				Dict       ,	_lowercase			:				List[Any]       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      GPTNeoXJapaneseForCausalLM(config=_lowercase      )
     model.to(_lowercase      )
     model.eval()
     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase       ,	labels=_lowercase      )
     self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.vocab_size)      )




   def 	__a						(    self			:				int       ,	_lowercase			:				Dict       ,	_lowercase			:				Optional[Any]       ,	_lowercase			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      True
     SCREAMING_SNAKE_CASE__										=      GPTNeoXJapaneseForCausalLM(config=_lowercase      )
     model.to(_lowercase      )
     model.eval()

     # first forward pass
     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase       ,	use_cache=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      outputs.past_key_values

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

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

     SCREAMING_SNAKE_CASE__										=      model(_lowercase       ,	attention_mask=_lowercase       ,	output_hidden_states=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      output_from_no_past["""hidden_states"""][0]
     SCREAMING_SNAKE_CASE__										=      model(
         _lowercase       ,	attention_mask=_lowercase       ,	past_key_values=_lowercase       ,	output_hidden_states=_lowercase       ,	)["""hidden_states"""][0]

     # select random slice
     SCREAMING_SNAKE_CASE__										=      ids_tensor((1,)       ,	output_from_past.shape[-1]      ).item()
     SCREAMING_SNAKE_CASE__										=      output_from_no_past[:, -3:, random_slice_idx].detach()
     SCREAMING_SNAKE_CASE__										=      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(_lowercase       ,	_lowercase       ,	atol=1E-3      )      )




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      config_and_inputs
     SCREAMING_SNAKE_CASE__										=      {"""input_ids""": input_ids, """attention_mask""": input_mask}
     return config, inputs_dict







@require_torch
class __snake_case				(						lowerCamelCase_   ,		lowerCamelCase_   ,		unittest.TestCase		):
   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 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      GPTNeoXJapaneseModelTester(self      )
     SCREAMING_SNAKE_CASE__										=      ConfigTester(self       ,	config_class=_lowercase       ,	hidden_size=37      )




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     self.config_tester.run_common_tests()




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(_lowercase       ,	_lowercase       ,	_lowercase      )




   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_decoder()
     self.model_tester.create_and_check_model_as_decoder(_lowercase       ,	_lowercase       ,	_lowercase      )




   def 	__a						(    self			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs_for_decoder()

     SCREAMING_SNAKE_CASE__										=      None

     self.model_tester.create_and_check_model_as_decoder(_lowercase       ,	_lowercase       ,	_lowercase      )




   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past_large_inputs(_lowercase       ,	_lowercase       ,	_lowercase      )




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_causal_lm(*_lowercase      )




   @slow
   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """abeja/gpt-neox-japanese-2.7b"""

     SCREAMING_SNAKE_CASE__										=      ["""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、"""]

     SCREAMING_SNAKE_CASE__										=      [
         """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。""",
     ]

     SCREAMING_SNAKE_CASE__										=      GPTNeoXJapaneseTokenizer.from_pretrained(_lowercase      )
     SCREAMING_SNAKE_CASE__										=      GPTNeoXJapaneseForCausalLM.from_pretrained(_lowercase      )

     SCREAMING_SNAKE_CASE__										=      []
     for prompt in prompts:
       SCREAMING_SNAKE_CASE__										=      tokenizer(_lowercase       ,	return_tensors="""pt"""      ).input_ids
       SCREAMING_SNAKE_CASE__										=      model.generate(_lowercase       ,	max_length=50      )
       SCREAMING_SNAKE_CASE__										=      tokenizer.batch_decode(_lowercase       ,	skip_special_tokens=_lowercase      )
       predicted_outputs += generated_string
     self.assertListEqual(_lowercase       ,	_lowercase      )
