






from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers.models.layoutlm.modeling_tf_layoutlm import (
	    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFLayoutLMForMaskedLM,
	    TFLayoutLMForQuestionAnswering,
	    TFLayoutLMForSequenceClassification,
	    TFLayoutLMForTokenClassification,
	    TFLayoutLMModel,
	)




class  UpperCamelCase_      :



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=13       ,			a=7       ,			a=True       ,			a=True       ,			a=True       ,			a=True       ,			a=99       ,			a=32       ,			a=2       ,			a=4       ,			a=37       ,			a="gelu"       ,			a=0.1       ,			a=0.1       ,			a=5_12       ,			a=16       ,			a=2       ,			a=0.02       ,			a=3       ,			a=4       ,			a=None       ,			a=10_00       ,			)       ->    List[Any]:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							seq_length
								snake_case_				=							is_training
								snake_case_				=							use_input_mask
								snake_case_				=							use_token_type_ids
								snake_case_				=							use_labels
								snake_case_				=							vocab_size
								snake_case_				=							hidden_size
								snake_case_				=							num_hidden_layers
								snake_case_				=							num_attention_heads
								snake_case_				=							intermediate_size
								snake_case_				=							hidden_act
								snake_case_				=							hidden_dropout_prob
								snake_case_				=							attention_probs_dropout_prob
								snake_case_				=							max_position_embeddings
								snake_case_				=							type_vocab_size
								snake_case_				=							type_sequence_label_size
								snake_case_				=							initializer_range
								snake_case_				=							num_labels
								snake_case_				=							num_choices
								snake_case_				=							scope
								snake_case_				=							range_bbox




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

								# convert bbox to numpy since TF does not support item assignment
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length, 4]       ,			self.range_bbox					).numpy()
								# Ensure that bbox is legal
								for i in range(bbox.shape[0]					):
															for j in range(bbox.shape[1]					):
																						if bbox[i, j, 3] < bbox[i, j, 1]:
																													snake_case_				=							bbox[i, j, 3]
																													snake_case_				=							bbox[i, j, 1]
																													snake_case_				=							t
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																													snake_case_				=							bbox[i, j, 2]
																													snake_case_				=							bbox[i, j, 0]
																													snake_case_				=							t
								snake_case_				=							tf.convert_to_tensor(a					)

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

								snake_case_				=							None
								if self.use_token_type_ids:
															snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.type_vocab_size					)

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

								snake_case_				=							LayoutLMConfig(
								    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_size=self.intermediate_size       ,			hidden_act=self.hidden_act       ,			hidden_dropout_prob=self.hidden_dropout_prob       ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,			max_position_embeddings=self.max_position_embeddings       ,			type_vocab_size=self.type_vocab_size       ,			initializer_range=self.initializer_range       ,			)

								return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    Dict:
								snake_case_				=							TFLayoutLMModel(config=a					)

								snake_case_				=							model(a       ,			a       ,			attention_mask=a       ,			token_type_ids=a					)
								snake_case_				=							model(a       ,			a       ,			token_type_ids=a					)
								snake_case_				=							model(a       ,			a					)
								self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size)					)
								self.parent.assertEqual(result.pooler_output.shape       ,			(self.batch_size, self.hidden_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    int:
								snake_case_				=							TFLayoutLMForMaskedLM(config=a					)

								snake_case_				=							model(a       ,			a       ,			attention_mask=a       ,			token_type_ids=a       ,			labels=a					)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.seq_length, self.vocab_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    Dict:
								snake_case_				=							self.num_labels
								snake_case_				=							TFLayoutLMForSequenceClassification(config=a					)

								snake_case_				=							model(a       ,			a       ,			attention_mask=a       ,			token_type_ids=a					)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.num_labels)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    str:
								snake_case_				=							self.num_labels
								snake_case_				=							TFLayoutLMForTokenClassification(config=a					)

								snake_case_				=							model(a       ,			a       ,			attention_mask=a       ,			token_type_ids=a       ,			labels=a					)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.seq_length, self.num_labels)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    List[Any]:
								snake_case_				=							TFLayoutLMForQuestionAnswering(config=a					)

								snake_case_				=							model(a       ,			a       ,			attention_mask=a       ,			token_type_ids=a					)
								self.parent.assertEqual(result.start_logits.shape       ,			(self.batch_size, self.seq_length)					)
								self.parent.assertEqual(result.end_logits.shape       ,			(self.batch_size, self.seq_length)					)






	def 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_				=							self.prepare_config_and_inputs()
								(
								    (
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		
								)				=							config_and_inputs
								snake_case_				=							{
								    'input_ids': input_ids,
								    'bbox': bbox,
								    'token_type_ids': token_type_ids,
								    'attention_mask': input_mask,
								}
								return config, inputs_dict






@require_tf
class  UpperCamelCase_      (			snake_case_       ,					snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= (
	    (
	        TFLayoutLMModel,
	        TFLayoutLMForMaskedLM,
	        TFLayoutLMForTokenClassification,
	        TFLayoutLMForSequenceClassification,
	        TFLayoutLMForQuestionAnswering,
	    )
	    if is_tf_available()
	    else ()
	)
	lowerCAmelCase													= (
	    {
	        '''feature-extraction''': TFLayoutLMModel,
	        '''fill-mask''': TFLayoutLMForMaskedLM,
	        '''text-classification''': TFLayoutLMForSequenceClassification,
	        '''token-classification''': TFLayoutLMForTokenClassification,
	        '''zero-shot''': TFLayoutLMForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	lowerCAmelCase													= False
	lowerCAmelCase													= True
	lowerCAmelCase													= 1_0




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							TFLayoutLMModelTester(self					)
								snake_case_				=							ConfigTester(self       ,			config_class=a       ,			hidden_size=37					)




	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								self.config_tester.run_common_tests()




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*a					)




	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*a					)




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*a					)




	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*a					)




	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*a					)




	@slow
	def 							_UpperCamelCase			(       self					)       ->    Dict:
								for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_				=							TFLayoutLMModel.from_pretrained(a					)
															self.assertIsNotNone(a					)






	@unittest.skip('Onnx compliancy broke with TF 2.10'					)
	def 							_UpperCamelCase			(       self					)       ->    str:
								pass






def 						__UpperCAmelCase			(     ):
							# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
							# fmt: off
							snake_case_				=							tf.convert_to_tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]])  # noqa: E231
							snake_case_				=							tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],])  # noqa: E231
							snake_case_				=							tf.convert_to_tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]])  # noqa: E231
							snake_case_				=							tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]])  # noqa: E231
							# these are sequence labels (i.e. at the token level)
							snake_case_				=							tf.convert_to_tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]])  # noqa: E231
							# fmt: on

							return input_ids, attention_mask, bbox, token_type_ids, labels




@require_tf
class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	@slow
	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								snake_case_				=							TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'					)

								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							prepare_layoutlm_batch_inputs()

								# forward pass
								snake_case_				=							model(input_ids=a       ,			bbox=a       ,			attention_mask=a       ,			token_type_ids=a					)

								# test the sequence output on [0, :3, :3]
								snake_case_				=							tf.convert_to_tensor(
								    [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]       ,			)

								self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]       ,			a       ,			atol=1E-3					)					)

								# test the pooled output on [1, :3]
								snake_case_				=							tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552]					)

								self.assertTrue(np.allclose(outputs.pooler_output[1, :3]       ,			a       ,			atol=1E-3					)					)




	@slow
	def 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:
								# initialize model with randomly initialized sequence classification head
								snake_case_				=							TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'       ,			num_labels=2					)

								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							prepare_layoutlm_batch_inputs()

								# forward pass
								snake_case_				=							model(
								    input_ids=a       ,			bbox=a       ,			attention_mask=a       ,			token_type_ids=a       ,			labels=tf.convert_to_tensor([1, 1]					)       ,			)

								# test whether we get a loss as a scalar
								snake_case_				=							outputs.loss
								snake_case_				=							(2,)
								self.assertEqual(loss.shape       ,			a					)

								# test the shape of the logits
								snake_case_				=							outputs.logits
								snake_case_				=							(2, 2)
								self.assertEqual(logits.shape       ,			a					)




	@slow
	def 							_UpperCamelCase			(       self					)       ->    int:
								# initialize model with randomly initialized token classification head
								snake_case_				=							TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'       ,			num_labels=13					)

								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							prepare_layoutlm_batch_inputs()

								# forward pass
								snake_case_				=							model(
								    input_ids=a       ,			bbox=a       ,			attention_mask=a       ,			token_type_ids=a       ,			labels=a					)

								# test the shape of the logits
								snake_case_				=							outputs.logits
								snake_case_				=							tf.convert_to_tensor((2, 25, 13)					)
								self.assertEqual(logits.shape       ,			a					)






	@slow
	def 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:
								# initialize model with randomly initialized token classification head
								snake_case_				=							TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'					)

								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							prepare_layoutlm_batch_inputs()

								# forward pass
								snake_case_				=							model(input_ids=a       ,			bbox=a       ,			attention_mask=a       ,			token_type_ids=a					)

								# test the shape of the logits
								snake_case_				=							tf.convert_to_tensor((2, 25)					)
								self.assertEqual(outputs.start_logits.shape       ,			a					)
								self.assertEqual(outputs.end_logits.shape       ,			a					)













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  UpperCamelCase_      :



	'''simple docstring'''




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




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size					)

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

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

								snake_case_				=							self.get_config()

								return config, input_ids, input_mask, token_labels




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								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 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.prepare_config_and_inputs()

								snake_case_				=							True

								return config, input_ids, input_mask, token_labels




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    Any:
								snake_case_				=							GPTNeoXJapaneseModel(config=a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a					)
								snake_case_				=							model(a					)
								self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size)					)




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




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a					)       ->    int:
								snake_case_				=							GPTNeoXJapaneseForCausalLM(config=a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a       ,			labels=a					)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.seq_length, self.vocab_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    Tuple:
								snake_case_				=							True
								snake_case_				=							GPTNeoXJapaneseForCausalLM(config=a					)
								model.to(a					)
								model.eval()

								# first forward pass
								snake_case_				=							model(a       ,			attention_mask=a       ,			use_cache=a					)
								snake_case_				=							outputs.past_key_values

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

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

								snake_case_				=							model(a       ,			attention_mask=a       ,			output_hidden_states=a					)
								snake_case_				=							output_from_no_past['hidden_states'][0]
								snake_case_				=							model(
								    a       ,			attention_mask=a       ,			past_key_values=a       ,			output_hidden_states=a       ,			)['hidden_states'][0]

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






	def 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs
								snake_case_				=							{'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict






@require_torch
class  UpperCamelCase_      (			snake_case_       ,					snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	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 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							GPTNeoXJapaneseModelTester(self					)
								snake_case_				=							ConfigTester(self       ,			config_class=a       ,			hidden_size=37					)




	def 							_UpperCamelCase			(       self					)       ->    str:
								self.config_tester.run_common_tests()




	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(a       ,			a       ,			a					)




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

								snake_case_				=							None

								self.model_tester.create_and_check_model_as_decoder(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*a					)






	@slow
	def 							_UpperCamelCase			(       self					)       ->    Any:
								snake_case_				=							'abeja/gpt-neox-japanese-2.7b'

								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、']

								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。',
								]

								snake_case_				=							GPTNeoXJapaneseTokenizer.from_pretrained(a					)
								snake_case_				=							GPTNeoXJapaneseForCausalLM.from_pretrained(a					)

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





