





"""simple docstring"""
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   :


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


					def 	lowerCamelCase       (					self			:Dict		):
												A		=  ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size		)

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

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

												A		=  self.get_config()

												return config, input_ids, input_mask, token_labels


					def 	lowerCamelCase       (					self			:Any		):
												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 	lowerCamelCase       (					self			:str		):
												A,	A,	A,	A		=  self.prepare_config_and_inputs()

												A		=  True

												return config, input_ids, input_mask, token_labels


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:Optional[int]		):
												A		=  GPTNeoXJapaneseModel(config=__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase     ,       attention_mask=__UpperCamelCase		)
												A		=  model(__UpperCamelCase		)
												self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)		)


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:int		):
												A		=  True
												A		=  GPTNeoXJapaneseModel(__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase     ,       attention_mask=__UpperCamelCase		)
												self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)		)


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:Tuple     ,       __UpperCamelCase			:List[Any]     ,       __UpperCamelCase			:Optional[Any]		):
												A		=  GPTNeoXJapaneseForCausalLM(config=__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(__UpperCamelCase     ,       attention_mask=__UpperCamelCase     ,       labels=__UpperCamelCase		)
												self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.seq_length, self.vocab_size)		)


					def 	lowerCamelCase       (					self			:Dict     ,       __UpperCamelCase			:Optional[int]     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Any		):
												A		=  True
												A		=  GPTNeoXJapaneseForCausalLM(config=__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()

												# first forward pass
												A		=  model(__UpperCamelCase     ,       attention_mask=__UpperCamelCase     ,       use_cache=__UpperCamelCase		)
												A		=  outputs.past_key_values

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

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

												A		=  model(__UpperCamelCase     ,       attention_mask=__UpperCamelCase     ,       output_hidden_states=__UpperCamelCase		)
												A		=  output_from_no_past["hidden_states"][0]
												A		=  model(
												    __UpperCamelCase     ,       attention_mask=__UpperCamelCase     ,       past_key_values=__UpperCamelCase     ,       output_hidden_states=__UpperCamelCase     ,       )["hidden_states"][0]

												# select random slice
												A		=  ids_tensor((1,)     ,       output_from_past.shape[-1]		).item()
												A		=  output_from_no_past[:, -3:, random_slice_idx].detach()
												A		=  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 	lowerCamelCase       (					self			:List[str]		):
												A		=  self.prepare_config_and_inputs()
												A,	A,	A,	A		=  config_and_inputs
												A		=  {"input_ids": input_ids, "attention_mask": input_mask}
												return config, inputs_dict







@require_torch
class 					_UpperCAmelCase   (	lowercase_     ,							lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
					UpperCamelCase      =							(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
					UpperCamelCase      =							(
					    {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
					    if is_torch_available()
					    else {}
					)
					UpperCamelCase      =							False
					UpperCamelCase      =							False
					UpperCamelCase      =							False
					UpperCamelCase      =							False


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  GPTNeoXJapaneseModelTester(self		)
												A		=  ConfigTester(self     ,       config_class=__UpperCamelCase     ,       hidden_size=37		)


					def 	lowerCamelCase       (					self			:str		):
												self.config_tester.run_common_tests()


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A,	A,	A,	A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Tuple		):
												A,	A,	A,	A		=  self.model_tester.prepare_config_and_inputs_for_decoder()
												self.model_tester.create_and_check_model_as_decoder(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												# This regression test was failing with PyTorch < 1.3
												A,	A,	A,	A		=  self.model_tester.prepare_config_and_inputs_for_decoder()

												A		=  None

												self.model_tester.create_and_check_model_as_decoder(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[str]		):
												A,	A,	A,	A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_decoder_model_past_large_inputs(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_causal_lm(*__UpperCamelCase		)


					@slow
					def 	lowerCamelCase       (					self			:int		):
												A		=  "abeja/gpt-neox-japanese-2.7b"

												A		=  ["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、"]

												A		=  [
												    "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。",
												]

												A		=  GPTNeoXJapaneseTokenizer.from_pretrained(__UpperCamelCase		)
												A		=  GPTNeoXJapaneseForCausalLM.from_pretrained(__UpperCamelCase		)

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









"""simple docstring"""
import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_rjieba
@require_tokenizers
class 					_UpperCAmelCase   (	lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							RoFormerTokenizer
					UpperCamelCase      =							RoFormerTokenizerFast
					UpperCamelCase      =							True
					UpperCamelCase      =							True


					def 	lowerCamelCase       (					self			:List[str]		):
												super().setUp()


					def 	lowerCamelCase       (					self			:int     ,       **__UpperCamelCase			:List[Any]		):
												return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Tuple     ,       **__UpperCamelCase			:Optional[int]		):
												return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												A		=  "永和服装饰品有限公司,今天天气非常好"
												A		=  "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
												return input_text, output_text


					def 	lowerCamelCase       (					self			:int		):
												A		=  self.get_tokenizer()
												A,	A		=  self.get_chinese_input_output_texts()
												A		=  tokenizer.tokenize(__UpperCamelCase		)

												self.assertListEqual(__UpperCamelCase     ,       output_text.split()		)

												A		=  tokens + [tokenizer.unk_token]
												A		=  [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:str		):
												A		=  self.get_rust_tokenizer()
												A,	A		=  self.get_chinese_input_output_texts()
												A		=  tokenizer.tokenize(__UpperCamelCase		)
												self.assertListEqual(__UpperCamelCase     ,       output_text.split()		)
												A		=  tokens + [tokenizer.unk_token]
												A		=  [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												pass


					def 	lowerCamelCase       (					self			:Tuple		):
												pass


					def 	lowerCamelCase       (					self			:List[str]		):
												pass


