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 __A     :

							'''simple docstring'''

							def __init__(				self   ,	_snake_case   ,	_snake_case=13   ,	_snake_case=7   ,	_snake_case=True   ,	_snake_case=True   ,	_snake_case=True   ,	_snake_case=True   ,	_snake_case=99   ,	_snake_case=32   ,	_snake_case=5   ,	_snake_case=4   ,	_snake_case=4   ,	_snake_case="gelu"   ,	_snake_case=0.0   ,	_snake_case=0.1   ,	_snake_case=True   ,	_snake_case=512   ,	_snake_case=16   ,	_snake_case=2   ,	_snake_case=0.02   ,	_snake_case=3   ,	_snake_case=4   ,	_snake_case=None   ,	):
												_lowerCAmelCase			:		Tuple          = parent
												_lowerCAmelCase			:		int          = batch_size
												_lowerCAmelCase			:		Tuple          = seq_length
												_lowerCAmelCase			:		List[str]          = is_training
												_lowerCAmelCase			:		List[Any]          = use_input_mask
												_lowerCAmelCase			:		str          = use_token_type_ids
												_lowerCAmelCase			:		Optional[Any]          = use_labels
												_lowerCAmelCase			:		List[Any]          = vocab_size
												_lowerCAmelCase			:		str          = hidden_size
												_lowerCAmelCase			:		List[str]          = num_hidden_layers
												_lowerCAmelCase			:		int          = num_attention_heads
												_lowerCAmelCase			:		Optional[int]          = intermediate_multiple_size
												_lowerCAmelCase			:		List[Any]          = hidden_act
												_lowerCAmelCase			:		Any          = hidden_dropout
												_lowerCAmelCase			:		Optional[Any]          = attention_dropout
												_lowerCAmelCase			:		List[Any]          = weight_tying
												_lowerCAmelCase			:		Optional[int]          = max_position_embeddings
												_lowerCAmelCase			:		Any          = type_vocab_size
												_lowerCAmelCase			:		Dict          = type_sequence_label_size
												_lowerCAmelCase			:		List[Any]          = initializer_range
												_lowerCAmelCase			:		Union[str, Any]          = num_labels
												_lowerCAmelCase			:		Union[str, Any]          = num_choices
												_lowerCAmelCase			:		Tuple          = scope






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		Optional[Any]          = ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size )

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

												_lowerCAmelCase			:		Tuple          = None
												if self.use_labels:
																	_lowerCAmelCase			:		List[str]          = ids_tensor([self.batch_size, self.seq_length]   ,	self.num_labels )

												_lowerCAmelCase			:		List[str]          = self.get_config()

												return config, input_ids, input_mask, token_labels






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												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=UpperCamelCase_   ,	initializer_range=self.initializer_range   ,	)






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		Optional[Any]          = self.prepare_config_and_inputs()

												_lowerCAmelCase			:		Dict          = True

												return config, input_ids, input_mask, token_labels






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






							def 	SCREAMING_SNAKE_CASE__			(				self   ,	_snake_case   ,	_snake_case   ,	_snake_case ):
												_lowerCAmelCase			:		int          = True
												_lowerCAmelCase			:		Union[str, Any]          = GPTNeoXJapaneseModel(UpperCamelCase_ )
												model.to(UpperCamelCase_ )
												model.eval()
												_lowerCAmelCase			:		Any          = model(UpperCamelCase_   ,	attention_mask=UpperCamelCase_ )
												self.parent.assertEqual(result.last_hidden_state.shape   ,	(self.batch_size, self.seq_length, self.hidden_size) )






							def 	SCREAMING_SNAKE_CASE__			(				self   ,	_snake_case   ,	_snake_case   ,	_snake_case   ,	_snake_case ):
												_lowerCAmelCase			:		Union[str, Any]          = GPTNeoXJapaneseForCausalLM(config=UpperCamelCase_ )
												model.to(UpperCamelCase_ )
												model.eval()
												_lowerCAmelCase			:		str          = model(UpperCamelCase_   ,	attention_mask=UpperCamelCase_   ,	labels=UpperCamelCase_ )
												self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.vocab_size) )






							def 	SCREAMING_SNAKE_CASE__			(				self   ,	_snake_case   ,	_snake_case   ,	_snake_case ):
												_lowerCAmelCase			:		Dict          = True
												_lowerCAmelCase			:		str          = GPTNeoXJapaneseForCausalLM(config=UpperCamelCase_ )
												model.to(UpperCamelCase_ )
												model.eval()

												# first forward pass
												_lowerCAmelCase			:		Optional[int]          = model(UpperCamelCase_   ,	attention_mask=UpperCamelCase_   ,	use_cache=UpperCamelCase_ )
												_lowerCAmelCase			:		Optional[int]          = outputs.past_key_values

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

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

												_lowerCAmelCase			:		Tuple          = model(UpperCamelCase_   ,	attention_mask=UpperCamelCase_   ,	output_hidden_states=UpperCamelCase_ )
												_lowerCAmelCase			:		int          = output_from_no_past['''hidden_states'''][0]
												_lowerCAmelCase			:		str          = model(
												    UpperCamelCase_   ,	attention_mask=UpperCamelCase_   ,	past_key_values=UpperCamelCase_   ,	output_hidden_states=UpperCamelCase_   ,	)['''hidden_states'''][0]

												# select random slice
												_lowerCAmelCase			:		List[str]          = ids_tensor((1,)   ,	output_from_past.shape[-1] ).item()
												_lowerCAmelCase			:		List[str]          = output_from_no_past[:, -3:, random_slice_idx].detach()
												_lowerCAmelCase			:		Union[str, Any]          = 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(UpperCamelCase_   ,	UpperCamelCase_   ,	atol=1E-3 ) )






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		Tuple          = self.prepare_config_and_inputs()
												_lowerCAmelCase			:		Union[str, Any]          = config_and_inputs
												_lowerCAmelCase			:		Any          = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
												return config, inputs_dict



@require_torch
class __A     (      A__		,A__		,unittest.TestCase						):

							'''simple docstring'''

							a_     =					(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
							a_     =					(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
							a_     =					(
							    {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
							    if is_torch_available()
							    else {}
							)
							a_     =					False
							a_     =					False
							a_     =					False
							a_     =					False






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		Optional[int]          = GPTNeoXJapaneseModelTester(self )
												_lowerCAmelCase			:		Optional[Any]          = ConfigTester(self   ,	config_class=UpperCamelCase_   ,	hidden_size=37 )






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												self.config_tester.run_common_tests()






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		Tuple          = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_ )






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		List[str]          = self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_model_as_decoder(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_ )






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		int          = self.model_tester.prepare_config_and_inputs_for_decoder()

												_lowerCAmelCase			:		Union[str, Any]          = None

												self.model_tester.create_and_check_model_as_decoder(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_ )






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		str          = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past_large_inputs(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_ )






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		Tuple          = self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_causal_lm(*UpperCamelCase_ )






							@slow
							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		List[Any]          = '''abeja/gpt-neox-japanese-2.7b'''

												_lowerCAmelCase			:		Union[str, Any]          = ['''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、''']

												_lowerCAmelCase			:		Union[str, Any]          = [
												    '''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。''',
												]

												_lowerCAmelCase			:		Optional[Any]          = GPTNeoXJapaneseTokenizer.from_pretrained(UpperCamelCase_ )
												_lowerCAmelCase			:		List[str]          = GPTNeoXJapaneseForCausalLM.from_pretrained(UpperCamelCase_ )

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










'''simple docstring'''
import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class   lowercase      (				A__      ,						unittest.TestCase   ):

		"""simple docstring"""

		_a       					=							LxmertTokenizer
		_a       					=							LxmertTokenizerFast
		_a       					=							True
		_a       					=							True




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							super().setUp()

							UpperCamelCase__  :Any				   =					[
							    '''[UNK]''',
							    '''[CLS]''',
							    '''[SEP]''',
							    '''want''',
							    '''##want''',
							    '''##ed''',
							    '''wa''',
							    '''un''',
							    '''runn''',
							    '''##ing''',
							    ''',''',
							    '''low''',
							    '''lowest''',
							]
							UpperCamelCase__  :str				   =					os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES['''vocab_file''']				)
							with open(self.vocab_file       ,  '''w'''       ,  encoding='''utf-8'''				) as vocab_writer:
												vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]				)				)




		def        lowerCAmelCase__							(						self       ,  UpperCamelCase_				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					'''UNwant\u00E9d,running'''
							UpperCamelCase__  :Union[str, Any]				   =					'''unwanted, running'''
							return input_text, output_text




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :List[Any]				   =					self.tokenizer_class(self.vocab_file				)

							UpperCamelCase__  :List[Any]				   =					tokenizer.tokenize('''UNwant\u00E9d,running'''				)
							self.assertListEqual(UpperCamelCase_       ,  ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']				)
							self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_				)       ,  [7, 4, 5, 10, 8, 9]				)




		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							if not self.test_rust_tokenizer:
												return

							UpperCamelCase__  :str				   =					self.get_tokenizer()
							UpperCamelCase__  :Union[str, Any]				   =					self.get_rust_tokenizer()

							UpperCamelCase__  :int				   =					'''I was born in 92000, and this is falsé.'''

							UpperCamelCase__  :Optional[Any]				   =					tokenizer.tokenize(UpperCamelCase_				)
							UpperCamelCase__  :str				   =					rust_tokenizer.tokenize(UpperCamelCase_				)
							self.assertListEqual(UpperCamelCase_       ,  UpperCamelCase_				)

							UpperCamelCase__  :Optional[Any]				   =					tokenizer.encode(UpperCamelCase_       ,  add_special_tokens=UpperCamelCase_				)
							UpperCamelCase__  :int				   =					rust_tokenizer.encode(UpperCamelCase_       ,  add_special_tokens=UpperCamelCase_				)
							self.assertListEqual(UpperCamelCase_       ,  UpperCamelCase_				)

							UpperCamelCase__  :Optional[int]				   =					self.get_rust_tokenizer()
							UpperCamelCase__  :Any				   =					tokenizer.encode(UpperCamelCase_				)
							UpperCamelCase__  :Optional[int]				   =					rust_tokenizer.encode(UpperCamelCase_				)
							self.assertListEqual(UpperCamelCase_       ,  UpperCamelCase_				)