import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_pytesseract_available():
   from PIL import Image

   from transformers import LayoutLMvaImageProcessor







class __snake_case				(						unittest.TestCase		):




   def __init__(    self			:				List[Any]       ,	_lowercase			:				Tuple       ,	_lowercase			:				Tuple=7       ,	_lowercase			:				str=3       ,	_lowercase			:				List[str]=18       ,	_lowercase			:				List[str]=30       ,	_lowercase			:				int=4_00       ,	_lowercase			:				Tuple=True       ,	_lowercase			:				int=None       ,	_lowercase			:				Dict=True       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      size if size is not None else {"""height""": 18, """width""": 18}
     SCREAMING_SNAKE_CASE__										=      parent
     SCREAMING_SNAKE_CASE__										=      batch_size
     SCREAMING_SNAKE_CASE__										=      num_channels
     SCREAMING_SNAKE_CASE__										=      image_size
     SCREAMING_SNAKE_CASE__										=      min_resolution
     SCREAMING_SNAKE_CASE__										=      max_resolution
     SCREAMING_SNAKE_CASE__										=      do_resize
     SCREAMING_SNAKE_CASE__										=      size
     SCREAMING_SNAKE_CASE__										=      apply_ocr




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}







@require_torch
@require_pytesseract
class __snake_case				(						lowerCamelCase_   ,		unittest.TestCase		):
   lowerCAmelCase_														=							LayoutLMvaImageProcessor if is_pytesseract_available() else None




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      LayoutLMvaImageProcessingTester(self      )




   @property
   def 	__a						(    self			:				int      ):



     """simple docstring"""






     return self.image_processor_tester.prepare_image_processor_dict()




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processing_class(**self.image_processor_dict      )
     self.assertTrue(hasattr(_lowercase       ,	"""do_resize"""      )      )
     self.assertTrue(hasattr(_lowercase       ,	"""size"""      )      )
     self.assertTrue(hasattr(_lowercase       ,	"""apply_ocr"""      )      )




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processing_class.from_dict(self.image_processor_dict      )
     self.assertEqual(image_processor.size       ,	{"""height""": 18, """width""": 18}      )

     SCREAMING_SNAKE_CASE__										=      self.image_processing_class.from_dict(self.image_processor_dict       ,	size=42      )
     self.assertEqual(image_processor.size       ,	{"""height""": 42, """width""": 42}      )




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     pass




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processing_class(**self.image_processor_dict      )
     # create random PIL images
     SCREAMING_SNAKE_CASE__										=      prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=_lowercase      )
     for image in image_inputs:
       self.assertIsInstance(_lowercase       ,	Image.Image      )

     # Test not batched input
     SCREAMING_SNAKE_CASE__										=      image_processing(image_inputs[0]       ,	return_tensors="""pt"""      )
     self.assertEqual(
         encoding.pixel_values.shape       ,	(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["""height"""],
             self.image_processor_tester.size["""width"""],
         )       ,	)

     self.assertIsInstance(encoding.words       ,	_lowercase      )
     self.assertIsInstance(encoding.boxes       ,	_lowercase      )

     # Test batched
     SCREAMING_SNAKE_CASE__										=      image_processing(_lowercase       ,	return_tensors="""pt"""      ).pixel_values
     self.assertEqual(
         encoded_images.shape       ,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["""height"""],
             self.image_processor_tester.size["""width"""],
         )       ,	)




   def 	__a						(    self			:				Optional[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processing_class(**self.image_processor_dict      )
     # create random numpy tensors
     SCREAMING_SNAKE_CASE__										=      prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=_lowercase       ,	numpify=_lowercase      )
     for image in image_inputs:
       self.assertIsInstance(_lowercase       ,	np.ndarray      )

     # Test not batched input
     SCREAMING_SNAKE_CASE__										=      image_processing(image_inputs[0]       ,	return_tensors="""pt"""      ).pixel_values
     self.assertEqual(
         encoded_images.shape       ,	(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["""height"""],
             self.image_processor_tester.size["""width"""],
         )       ,	)

     # Test batched
     SCREAMING_SNAKE_CASE__										=      image_processing(_lowercase       ,	return_tensors="""pt"""      ).pixel_values
     self.assertEqual(
         encoded_images.shape       ,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["""height"""],
             self.image_processor_tester.size["""width"""],
         )       ,	)




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.image_processing_class(**self.image_processor_dict      )
     # create random PyTorch tensors
     SCREAMING_SNAKE_CASE__										=      prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=_lowercase       ,	torchify=_lowercase      )
     for image in image_inputs:
       self.assertIsInstance(_lowercase       ,	torch.Tensor      )

     # Test not batched input
     SCREAMING_SNAKE_CASE__										=      image_processing(image_inputs[0]       ,	return_tensors="""pt"""      ).pixel_values
     self.assertEqual(
         encoded_images.shape       ,	(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["""height"""],
             self.image_processor_tester.size["""width"""],
         )       ,	)

     # Test batched
     SCREAMING_SNAKE_CASE__										=      image_processing(_lowercase       ,	return_tensors="""pt"""      ).pixel_values
     self.assertEqual(
         encoded_images.shape       ,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["""height"""],
             self.image_processor_tester.size["""width"""],
         )       ,	)




   def 	__a						(    self			:				Tuple      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      LayoutLMvaImageProcessor()

     from datasets import load_dataset

     SCREAMING_SNAKE_CASE__										=      load_dataset("""hf-internal-testing/fixtures_docvqa"""       ,	split="""test"""      )

     SCREAMING_SNAKE_CASE__										=      Image.open(ds[0]["""file"""]      ).convert("""RGB"""      )

     SCREAMING_SNAKE_CASE__										=      image_processing(_lowercase       ,	return_tensors="""pt"""      )

     self.assertEqual(encoding.pixel_values.shape       ,	(1, 3, 2_24, 2_24)      )
     self.assertEqual(len(encoding.words      )       ,	len(encoding.boxes      )      )

     # fmt: off
     # the words and boxes were obtained with Tesseract 4.1.1
     SCREAMING_SNAKE_CASE__										=      [["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]]  # noqa: E231
     SCREAMING_SNAKE_CASE__										=      [[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
     # fmt: on

     self.assertListEqual(encoding.words       ,	_lowercase      )
     self.assertListEqual(encoding.boxes       ,	_lowercase      )

     # with apply_OCR = False
     SCREAMING_SNAKE_CASE__										=      LayoutLMvaImageProcessor(apply_ocr=_lowercase      )

     SCREAMING_SNAKE_CASE__										=      image_processing(_lowercase       ,	return_tensors="""pt"""      )

     self.assertEqual(encoding.pixel_values.shape       ,	(1, 3, 2_24, 2_24)      )

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      )
