






from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

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


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
	    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
	    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
	    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
	    LayoutLMvaConfig,
	    TFLayoutLMvaForQuestionAnswering,
	    TFLayoutLMvaForSequenceClassification,
	    TFLayoutLMvaForTokenClassification,
	    TFLayoutLMvaModel,
	)

if is_vision_available():
	from PIL import Image

	from transformers import LayoutLMvaImageProcessor




class  UpperCamelCase_      :



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=2       ,			a=3       ,			a=4       ,			a=2       ,			a=7       ,			a=True       ,			a=True       ,			a=True       ,			a=True       ,			a=99       ,			a=36       ,			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=6       ,			a=6       ,			a=3       ,			a=4       ,			a=None       ,			a=10_00       ,			)       ->    List[str]:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							num_channels
								snake_case_				=							image_size
								snake_case_				=							patch_size
								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_				=							coordinate_size
								snake_case_				=							shape_size
								snake_case_				=							num_labels
								snake_case_				=							num_choices
								snake_case_				=							scope
								snake_case_				=							range_bbox

								# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
								snake_case_				=							text_seq_length
								snake_case_				=							(image_size // patch_size) ** 2 + 1
								snake_case_				=							self.text_seq_length + self.image_seq_length




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_				=							ids_tensor([self.batch_size, self.text_seq_length]       ,			self.vocab_size					)

								snake_case_				=							ids_tensor([self.batch_size, self.text_seq_length, 4]       ,			self.range_bbox					)
								snake_case_				=							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_				=							tmp_coordinate
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																													snake_case_				=							bbox[i, j, 2]
																													snake_case_				=							bbox[i, j, 0]
																													snake_case_				=							tmp_coordinate
								snake_case_				=							tf.constant(a					)

								snake_case_				=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

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

								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.text_seq_length]       ,			self.num_labels					)

								snake_case_				=							LayoutLMvaConfig(
								    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       ,			coordinate_size=self.coordinate_size       ,			shape_size=self.shape_size       ,			input_size=self.image_size       ,			patch_size=self.patch_size       ,			)

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




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

								# text + image
								snake_case_				=							model(a       ,			pixel_values=a       ,			training=a					)
								snake_case_				=							model(
								    a       ,			bbox=a       ,			pixel_values=a       ,			attention_mask=a       ,			token_type_ids=a       ,			training=a       ,			)
								snake_case_				=							model(a       ,			bbox=a       ,			pixel_values=a       ,			training=a					)

								self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size)					)

								# text only
								snake_case_				=							model(a       ,			training=a					)
								self.parent.assertEqual(
								    result.last_hidden_state.shape       ,			(self.batch_size, self.text_seq_length, self.hidden_size)					)

								# image only
								snake_case_				=							model({'pixel_values': pixel_values}       ,			training=a					)
								self.parent.assertEqual(
								    result.last_hidden_state.shape       ,			(self.batch_size, self.image_seq_length, self.hidden_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    List[str]:
								snake_case_				=							self.num_labels
								snake_case_				=							TFLayoutLMvaForSequenceClassification(config=a					)
								snake_case_				=							model(
								    a       ,			bbox=a       ,			pixel_values=a       ,			attention_mask=a       ,			token_type_ids=a       ,			labels=a       ,			training=a       ,			)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.num_labels)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    Any:
								snake_case_				=							self.num_labels
								snake_case_				=							TFLayoutLMvaForTokenClassification(config=a					)
								snake_case_				=							model(
								    a       ,			bbox=a       ,			pixel_values=a       ,			attention_mask=a       ,			token_type_ids=a       ,			labels=a       ,			training=a       ,			)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.text_seq_length, self.num_labels)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    Tuple:
								snake_case_				=							2
								snake_case_				=							TFLayoutLMvaForQuestionAnswering(config=a					)
								snake_case_				=							model(
								    a       ,			bbox=a       ,			pixel_values=a       ,			attention_mask=a       ,			token_type_ids=a       ,			start_positions=a       ,			end_positions=a       ,			training=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					)       ->    Optional[int]:
								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,
								    'pixel_values': pixel_values,
								    '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													= (
	    (
	        TFLayoutLMvaModel,
	        TFLayoutLMvaForQuestionAnswering,
	        TFLayoutLMvaForSequenceClassification,
	        TFLayoutLMvaForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	lowerCAmelCase													= (
	    {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
	    if is_tf_available()
	    else {}
	)

	lowerCAmelCase													= False
	lowerCAmelCase													= False
	lowerCAmelCase													= False




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a       ,			a					)       ->    Any:
								return True




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a=False					)       ->    dict:
								snake_case_				=							copy.deepcopy(a					)

								if model_class in get_values(a					):
															snake_case_				=							{
															    k: tf.tile(tf.expand_dims(a       ,			1					)       ,			(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)					)
															    if isinstance(a       ,			tf.Tensor					) and v.ndim > 0
															    else v
															    for k, v in inputs_dict.items()
															}

								if return_labels:
															if model_class in get_values(a					):
																						snake_case_				=							tf.ones(self.model_tester.batch_size       ,			dtype=tf.intaa					)
															elif model_class in get_values(a					):
																						snake_case_				=							tf.zeros(self.model_tester.batch_size       ,			dtype=tf.intaa					)
																						snake_case_				=							tf.zeros(self.model_tester.batch_size       ,			dtype=tf.intaa					)
															elif model_class in get_values(a					):
																						snake_case_				=							tf.zeros(self.model_tester.batch_size       ,			dtype=tf.intaa					)
															elif model_class in get_values(a					):
																						snake_case_				=							tf.zeros(
																						    (self.model_tester.batch_size, self.model_tester.text_seq_length)       ,			dtype=tf.intaa					)

								return inputs_dict




	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								snake_case_				=							TFLayoutLMvaModelTester(self					)
								snake_case_				=							ConfigTester(self       ,			config_class=a       ,			hidden_size=37					)




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




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
															snake_case_				=							model_class(a					)
															if getattr(a       ,			'hf_compute_loss'       ,			a					):
																						# The number of elements in the loss should be the same as the number of elements in the label
																						snake_case_				=							self._prepare_for_class(inputs_dict.copy()       ,			a       ,			return_labels=a					)
																						snake_case_				=							prepared_for_class[
																						    sorted(prepared_for_class.keys() - inputs_dict.keys()       ,			reverse=a					)[0]
																						]
																						snake_case_				=							added_label.shape.as_list()[:1]

																						# Test that model correctly compute the loss with kwargs
																						snake_case_				=							self._prepare_for_class(inputs_dict.copy()       ,			a       ,			return_labels=a					)
																						snake_case_				=							prepared_for_class.pop('input_ids'					)

																						snake_case_				=							model(a       ,			**a					)[0]
																						self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)

																						# Test that model correctly compute the loss when we mask some positions
																						snake_case_				=							self._prepare_for_class(inputs_dict.copy()       ,			a       ,			return_labels=a					)
																						snake_case_				=							prepared_for_class.pop('input_ids'					)
																						if "labels" in prepared_for_class:
																													snake_case_				=							prepared_for_class['labels'].numpy()
																													if len(labels.shape					) > 1 and labels.shape[1] != 1:
																																				snake_case_				=							-1_00
																																				snake_case_				=							tf.convert_to_tensor(a					)
																																				snake_case_				=							model(a       ,			**a					)[0]
																																				self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)
																																				self.assertTrue(not np.any(np.isnan(loss.numpy()					)					)					)

                # Test that model correctly compute the loss with a dict
																						snake_case_				=							self._prepare_for_class(inputs_dict.copy()       ,			a       ,			return_labels=a					)
																						snake_case_				=							model(a					)[0]
																						self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)

																						# Test that model correctly compute the loss with a tuple
																						snake_case_				=							self._prepare_for_class(inputs_dict.copy()       ,			a       ,			return_labels=a					)

																						# Get keys that were added with the _prepare_for_class function
																						snake_case_				=							prepared_for_class.keys() - inputs_dict.keys()
																						snake_case_				=							inspect.signature(model.call					).parameters
																						snake_case_				=							list(signature.keys()					)

																						# Create a dictionary holding the location of the tensors in the tuple
																						snake_case_				=							{0: 'input_ids'}
																						for label_key in label_keys:
																													snake_case_				=							signature_names.index(a					)
																													snake_case_				=							label_key
																						snake_case_				=							sorted(tuple_index_mapping.items()					)
																						# Initialize a list with their default values, update the values and convert to a tuple
																						snake_case_				=							[]

																						for name in signature_names:
																													if name != "kwargs":
																																				list_input.append(signature[name].default					)

																						for index, value in sorted_tuple_index_mapping:
																													snake_case_				=							prepared_for_class[value]

																						snake_case_				=							tuple(a					)

																						# Send to model
																						snake_case_				=							model(tuple_input[:-1]					)[0]

																						self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)




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




	def 							_UpperCamelCase			(       self					)       ->    Any:
								(
								    (
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		(
								    snake_case_
								)			,		
								)				=							self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
															snake_case_				=							type
															self.model_tester.create_and_check_model(a       ,			a       ,			a       ,			a       ,			a       ,			a					)




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




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




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






	@slow
	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case_				=							TFLayoutLMvaModel.from_pretrained(a					)
															self.assertIsNotNone(a					)






def 						__UpperCAmelCase			(     ):
							snake_case_				=							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
							return image




@require_tf
class  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	@cached_property
	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								return LayoutLMvaImageProcessor(apply_ocr=a					) if is_vision_available() else None






	@slow
	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'					)

								snake_case_				=							self.default_image_processor
								snake_case_				=							prepare_img()
								snake_case_				=							image_processor(images=a       ,			return_tensors='tf'					).pixel_values

								snake_case_				=							tf.constant([[1, 2]]					)
								snake_case_				=							tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]					)       ,			axis=0					)

								# forward pass
								snake_case_				=							model(input_ids=a       ,			bbox=a       ,			pixel_values=a       ,			training=a					)

								# verify the logits
								snake_case_				=							(1, 1_99, 7_68)
								self.assertEqual(outputs.last_hidden_state.shape       ,			a					)

								snake_case_				=							tf.constant(
								    [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]					)

								self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]       ,			a       ,			atol=1E-4					)					)













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					)





