






import math
import unittest

from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
         BioGptForCausalLM,
         BioGptForSequenceClassification,
         BioGptForTokenClassification,
         BioGptModel,
         BioGptTokenizer,
     )
     from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__			,						a__=13			,						a__=7			,						a__=True			,						a__=True			,						a__=False			,						a__=True			,						a__=99			,						a__=32			,						a__=5			,						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			,						) ->      Tuple:






            '''simple docstring'''





            __snake_case			:int   =						parent
            __snake_case			:Optional[int]   =						batch_size
            __snake_case			:int   =						seq_length
            __snake_case			:Dict   =						is_training
            __snake_case			:List[str]   =						use_input_mask
            __snake_case			:List[Any]   =						use_token_type_ids
            __snake_case			:str   =						use_labels
            __snake_case			:List[str]   =						vocab_size
            __snake_case			:Tuple   =						hidden_size
            __snake_case			:Any   =						num_hidden_layers
            __snake_case			:Union[str, Any]   =						num_attention_heads
            __snake_case			:Optional[int]   =						intermediate_size
            __snake_case			:int   =						hidden_act
            __snake_case			:int   =						hidden_dropout_prob
            __snake_case			:List[str]   =						attention_probs_dropout_prob
            __snake_case			:Dict   =						max_position_embeddings
            __snake_case			:Optional[Any]   =						type_vocab_size
            __snake_case			:List[str]   =						type_sequence_label_size
            __snake_case			:List[Any]   =						initializer_range
            __snake_case			:Any   =						num_labels
            __snake_case			:Union[str, Any]   =						num_choices
            __snake_case			:Tuple   =						scope



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:int   =						ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size    )

            __snake_case			:str   =						None
            if self.use_input_mask:
                 __snake_case			:List[Any]   =						random_attention_mask([self.batch_size, self.seq_length]    )

            __snake_case			:Any   =						None
            if self.use_token_type_ids:
                 __snake_case			:Union[str, Any]   =						ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size    )

            __snake_case			:Dict   =						None
            __snake_case			:Optional[int]   =						None
            __snake_case			:int   =						None
            if self.use_labels:
                 __snake_case			:Dict   =						ids_tensor([self.batch_size]			,						self.type_sequence_label_size    )
                 __snake_case			:Optional[Any]   =						ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels    )
                 __snake_case			:Union[str, Any]   =						ids_tensor([self.batch_size]			,						self.num_choices    )

            __snake_case			:str   =						self.get_config()

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



       def 					__lowercase						(							self    ) ->      Tuple:






            '''simple docstring'''





            return BioGptConfig(
                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			,						is_decoder=a__			,						initializer_range=self.initializer_range			,						)



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__			,						a__			,						a__			,						a__    ) ->      str:






            '''simple docstring'''





            __snake_case			:Any   =						BioGptModel(config=a__    )
            model.to(a__    )
            model.eval()
            __snake_case			:Dict   =						model(a__			,						attention_mask=a__    )
            __snake_case			:Optional[int]   =						model(a__    )
            self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__			,						a__			,						a__			,						a__			,						a__			,						a__			,						) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:Tuple   =						BioGptForCausalLM(config=a__    )
            model.to(a__    )
            model.eval()
            __snake_case			:Optional[Any]   =						model(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 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__			,						a__			,						*a__    ) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:Dict   =						BioGptModel(config=a__    )
            model.to(a__    )
            model.eval()

            # create attention mask
            __snake_case			:Dict   =						torch.ones(input_ids.shape			,						dtype=torch.long			,						device=a__    )
            __snake_case			:int   =						self.seq_length // 2
            __snake_case			:Any   =						0

            # first forward pass
            __snake_case			:Optional[int]   =						model(a__			,						attention_mask=a__    ).to_tuple()

            # create hypothetical next token and extent to next_input_ids
            __snake_case			:Dict   =						ids_tensor((self.batch_size, 1)			,						config.vocab_size    )

            # change a random masked slice from input_ids
            __snake_case			:List[Any]   =						ids_tensor((1,)			,						a__    ).item() + 1
            __snake_case			:int   =						ids_tensor((self.batch_size, 1)			,						config.vocab_size    ).squeeze(-1    )
            __snake_case			:str   =						random_other_next_tokens

            # append to next input_ids and attn_mask
            __snake_case			:Union[str, Any]   =						torch.cat([input_ids, next_tokens]			,						dim=-1    )
            __snake_case			:Union[str, Any]   =						torch.cat(
                [attn_mask, torch.ones((attn_mask.shape[0], 1)			,						dtype=torch.long			,						device=a__    )]			,						dim=1			,						)

            # get two different outputs
            __snake_case			:Any   =						model(a__			,						attention_mask=a__    )["""last_hidden_state"""]
            __snake_case			:int   =						model(a__			,						past_key_values=a__			,						attention_mask=a__    )["""last_hidden_state"""]

            # select random slice
            __snake_case			:Optional[Any]   =						ids_tensor((1,)			,						output_from_past.shape[-1]    ).item()
            __snake_case			:Optional[int]   =						output_from_no_past[:, -1, random_slice_idx].detach()
            __snake_case			:Any   =						output_from_past[:, 0, random_slice_idx].detach()

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(a__			,						a__			,						atol=1e-3    )    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__			,						a__			,						*a__    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:Optional[int]   =						BioGptModel(config=a__    ).to(a__    ).eval()

            __snake_case			:Any   =						torch.ones(input_ids.shape			,						dtype=torch.long			,						device=a__    )

            # first forward pass
            __snake_case			:int   =						model(a__			,						attention_mask=a__			,						use_cache=a__    )

            __snake_case			:List[Any]   =						outputs.to_tuple()

            # create hypothetical multiple next token and extent to next_input_ids
            __snake_case			:Tuple   =						ids_tensor((self.batch_size, 3)			,						config.vocab_size    )
            __snake_case			:List[str]   =						ids_tensor((self.batch_size, 3)			,						2    )

            # append to next input_ids and
            __snake_case			:int   =						torch.cat([input_ids, next_tokens]			,						dim=-1    )
            __snake_case			:Any   =						torch.cat([attention_mask, next_attn_mask]			,						dim=-1    )

            __snake_case			:Dict   =						model(a__			,						attention_mask=a__    )["""last_hidden_state"""]
            __snake_case			:Union[str, Any]   =						model(a__			,						attention_mask=a__			,						past_key_values=a__    )[
                """last_hidden_state"""
            ]

            # select random slice
            __snake_case			:Union[str, Any]   =						ids_tensor((1,)			,						output_from_past.shape[-1]    ).item()
            __snake_case			:Tuple   =						output_from_no_past[:, -3:, random_slice_idx].detach()
            __snake_case			:Tuple   =						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 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__			,						a__			,						*a__			,						a__=False    ) ->      Dict:






            '''simple docstring'''





            __snake_case			:List[str]   =						BioGptForCausalLM(a__    )
            model.to(a__    )
            if gradient_checkpointing:
                 model.gradient_checkpointing_enable()

            __snake_case			:Any   =						model(a__			,						labels=a__    )
            self.parent.assertEqual(result.loss.shape			,						()    )
            self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size)    )
            result.loss.backward()



       def 					__lowercase						(							self			,						a__			,						*a__    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:Union[str, Any]   =						BioGptModel(a__    )
            __snake_case			:int   =						model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers    )
            for key in model.state_dict().keys():
                 if "c_proj" in key and "weight" in key:
                      self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key]    ) - model_std    )			,						0.0_01    )
                      self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key]    ) - 0.0    )			,						0.01    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__			,						a__			,						*a__    ) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:Union[str, Any]   =						self.num_labels
            __snake_case			:Union[str, Any]   =						BioGptForTokenClassification(a__    )
            model.to(a__    )
            model.eval()
            __snake_case			:Optional[Any]   =						model(a__			,						attention_mask=a__			,						token_type_ids=a__    )
            self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:List[str]   =						self.prepare_config_and_inputs()
            (
                __snake_case 
            )			:str   =						config_and_inputs
            __snake_case			:Optional[int]   =						{"""input_ids""": input_ids, """attention_mask""": input_mask}
            return config, inputs_dict




@require_torch
class 			snake_case__   (							lowercase_	,    lowercase_	,    lowercase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				List[str] 				= (
           (BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
           if is_torch_available()
           else ()
       )
       lowerCamelCase							:				Tuple 				= (BioGptForCausalLM,) if is_torch_available() else ()
       lowerCamelCase							:				Union[str, Any] 				= (
           {
               "feature-extraction": BioGptModel,
               "text-classification": BioGptForSequenceClassification,
               "text-generation": BioGptForCausalLM,
               "token-classification": BioGptForTokenClassification,
               "zero-shot": BioGptForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       lowerCamelCase							:				List[Any] 				= False



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[Any]   =						BioGptModelTester(self    )
            __snake_case			:str   =						ConfigTester(self			,						config_class=a__			,						hidden_size=37    )



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            self.config_tester.run_common_tests()



       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:Optional[Any]   =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*a__    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Optional[Any]   =						self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                 __snake_case			:Any   =						type
                 self.model_tester.create_and_check_model(*a__    )



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:Optional[Any]   =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_attention_mask_past(*a__    )



       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            __snake_case			:List[str]   =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_forward_and_backwards(*a__			,						gradient_checkpointing=a__    )



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Optional[int]   =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_model_past_large_inputs(*a__    )



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:Union[str, Any]   =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_weight_initialization(*a__    )



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:str   =						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_biogpt_for_token_classification(*a__    )



       @slow
       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:str   =						BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""    )
            model.to(a__    )
            __snake_case			:Tuple   =						BioGptTokenizer.from_pretrained("""microsoft/biogpt"""    )

            __snake_case			:str   =						"""left"""

            # Define PAD Token = EOS Token = 50256
            __snake_case			:Dict   =						tokenizer.eos_token
            __snake_case			:Optional[int]   =						model.config.eos_token_id

            # use different length sentences to test batching
            __snake_case			:str   =						[
                """Hello, my dog is a little""",
                """Today, I""",
            ]

            __snake_case			:Any   =						tokenizer(a__			,						return_tensors="""pt"""			,						padding=a__    )
            __snake_case			:Optional[int]   =						inputs["""input_ids"""].to(a__    )

            __snake_case			:str   =						model.generate(
                input_ids=a__			,						attention_mask=inputs["""attention_mask"""].to(a__    )			,						)

            __snake_case			:List[str]   =						tokenizer(sentences[0]			,						return_tensors="""pt"""    ).input_ids.to(a__    )
            __snake_case			:Dict   =						model.generate(input_ids=a__    )

            __snake_case			:List[Any]   =						inputs_non_padded.shape[-1] - inputs["""attention_mask"""][-1].long().sum().cpu().item()
            __snake_case			:int   =						tokenizer(sentences[1]			,						return_tensors="""pt"""    ).input_ids.to(a__    )
            __snake_case			:Any   =						model.generate(input_ids=a__			,						max_length=model.config.max_length - num_paddings    )

            __snake_case			:List[Any]   =						tokenizer.batch_decode(a__			,						skip_special_tokens=a__    )
            __snake_case			:Any   =						tokenizer.decode(output_non_padded[0]			,						skip_special_tokens=a__    )
            __snake_case			:str   =						tokenizer.decode(output_padded[0]			,						skip_special_tokens=a__    )

            __snake_case			:Union[str, Any]   =						[
                """Hello, my dog is a little bit bigger than a little bit.""",
                """Today, I have a good idea of how to use the information""",
            ]
            self.assertListEqual(a__			,						a__    )
            self.assertListEqual(a__			,						[non_padded_sentence, padded_sentence]    )



       @slow
       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __snake_case			:Optional[int]   =						BioGptModel.from_pretrained(a__    )
                 self.assertIsNotNone(a__    )



       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:str   =						self.model_tester.prepare_config_and_inputs_for_common()
            __snake_case			:Optional[int]   =						3
            __snake_case			:Optional[Any]   =						input_dict["""input_ids"""]
            __snake_case			:str   =						input_ids.ne(1    ).to(a__    )
            __snake_case			:Union[str, Any]   =						ids_tensor([self.model_tester.batch_size]			,						self.model_tester.type_sequence_label_size    )
            __snake_case			:int   =						BioGptForSequenceClassification(a__    )
            model.to(a__    )
            model.eval()
            __snake_case			:int   =						model(a__			,						attention_mask=a__			,						labels=a__    )
            self.assertEqual(result.logits.shape			,						(self.model_tester.batch_size, self.model_tester.num_labels)    )



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:List[str]   =						self.model_tester.prepare_config_and_inputs_for_common()
            __snake_case			:Union[str, Any]   =						3
            __snake_case			:Tuple   =						"""multi_label_classification"""
            __snake_case			:Dict   =						input_dict["""input_ids"""]
            __snake_case			:List[Any]   =						input_ids.ne(1    ).to(a__    )
            __snake_case			:Optional[int]   =						ids_tensor(
                [self.model_tester.batch_size, config.num_labels]			,						self.model_tester.type_sequence_label_size    ).to(torch.float    )
            __snake_case			:List[Any]   =						BioGptForSequenceClassification(a__    )
            model.to(a__    )
            model.eval()
            __snake_case			:List[str]   =						model(a__			,						attention_mask=a__			,						labels=a__    )
            self.assertEqual(result.logits.shape			,						(self.model_tester.batch_size, self.model_tester.num_labels)    )




@require_torch
class 			snake_case__   (							unittest.TestCase):

       '''simple docstring'''



       @slow
       def 					__lowercase						(							self    ) ->      int:






            '''simple docstring'''





            __snake_case			:Optional[int]   =						BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""    )
            __snake_case			:Any   =						torch.tensor([[2, 48_05, 9, 6_56, 21]]    )
            __snake_case			:List[str]   =						model(a__    )[0]

            __snake_case			:Any   =						4_23_84

            __snake_case			:Any   =						torch.Size((1, 5, vocab_size)    )
            self.assertEqual(output.shape			,						a__    )

            __snake_case			:int   =						torch.tensor(
                [[[-9.52_36, -9.89_18, 10.45_57], [-11.04_69, -9.64_23, 8.10_22], [-8.86_64, -7.88_26, 5.53_25]]]    )

            self.assertTrue(torch.allclose(output[:, :3, :3]			,						a__			,						atol=1e-4    )    )



       @slow
       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:List[str]   =						BioGptTokenizer.from_pretrained("""microsoft/biogpt"""    )
            __snake_case			:Optional[int]   =						BioGptForCausalLM.from_pretrained("""microsoft/biogpt"""    )
            model.to(a__    )

            torch.manual_seed(0    )
            __snake_case			:Optional[int]   =						tokenizer("""COVID-19 is"""			,						return_tensors="""pt"""    ).to(a__    )
            __snake_case			:Union[str, Any]   =						model.generate(
                **a__			,						min_length=1_00			,						max_length=10_24			,						num_beams=5			,						early_stopping=a__			,						)
            __snake_case			:List[str]   =						tokenizer.decode(output_ids[0]			,						skip_special_tokens=a__    )

            __snake_case			:int   =						(
                """COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"""
                """ causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"""
                """ territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"""
                """ and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"""
                """ more than 800,000 deaths."""
            )
            self.assertEqual(a__			,						a__    )










import json
import os
import unittest

from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
    VOCAB_FILES_NAMES,
    GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 			snake_case__   (							lowercase_	,    unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase							:				Optional[int] 				= GPTSanJapaneseTokenizer
       lowerCamelCase							:				Any 				= False
       lowerCamelCase							:				Optional[Any] 				= {"do_clean_text": False, "add_prefix_space": False}



       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            super().setUp()

            # fmt: off
            __snake_case			:str          =						["""こん""", """こんに""", """にちは""", """ばんは""", """世界,㔺界""", """、""", """。""", """<BR>""", """<SP>""", """<TAB>""", """<URL>""", """<EMAIL>""", """<TEL>""", """<DATE>""", """<PRICE>""", """<BLOCK>""", """<KIGOU>""", """<U2000U2BFF>""", """<|emoji1|>""", """<unk>""", """<|bagoftoken|>""", """<|endoftext|>"""]
            # fmt: on
            __snake_case			:Optional[Any]          =						{"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
            __snake_case			:Optional[int]          =						{"""unk_token""": """<unk>"""}

            __snake_case			:Optional[Any]          =						os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES["""vocab_file"""]    )
            __snake_case			:List[str]          =						os.path.join(self.tmpdirname			,						VOCAB_FILES_NAMES["""emoji_file"""]    )
            with open(self.vocab_file			,						"""w"""			,						encoding="""utf-8"""    ) as vocab_writer:
                 vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]    )    )
            with open(self.emoji_file			,						"""w"""    ) as emoji_writer:
                 emoji_writer.write(json.dumps(a__    )    )



       def 					__lowercase						(							self			,						**a__    ) ->      Optional[Any]:






            '''simple docstring'''





            kwargs.update(self.special_tokens_map    )
            return GPTSanJapaneseTokenizer.from_pretrained(self.tmpdirname			,						**a__    )



       def 					__lowercase						(							self			,						a__    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Tuple          =						"""こんにちは、世界。 \nこんばんは、㔺界。😀"""
            __snake_case			:Union[str, Any]          =						"""こんにちは、世界。 \nこんばんは、世界。😀"""
            return input_text, output_text



       def 					__lowercase						(							self			,						a__    ) ->      List[str]:






            '''simple docstring'''





            __snake_case       , __snake_case			:Dict          =						self.get_input_output_texts(a__    )
            __snake_case			:Dict          =						tokenizer.encode(a__			,						add_special_tokens=a__    )
            __snake_case			:int          =						tokenizer.decode(a__			,						clean_up_tokenization_spaces=a__    )
            return text, ids



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            pass  # TODO add if relevant



       def 					__lowercase						(							self    ) ->      Optional[Any]:






            '''simple docstring'''





            pass  # TODO add if relevant



       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            pass  # TODO add if relevant



       def 					__lowercase						(							self    ) ->      str:






            '''simple docstring'''





            __snake_case			:Optional[int]          =						self.get_tokenizer()

            # Testing tokenization
            __snake_case			:int          =						"""こんにちは、世界。　こんばんは、㔺界。"""
            __snake_case			:Dict          =						["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
            __snake_case			:Optional[Any]          =						tokenizer.tokenize(a__    )
            self.assertListEqual(a__			,						a__    )

            # Testing conversion to ids without special tokens
            __snake_case			:List[str]          =						[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
            __snake_case			:Any          =						tokenizer.convert_tokens_to_ids(a__    )
            self.assertListEqual(a__			,						a__    )

            # Testing conversion to ids with special tokens
            __snake_case			:Tuple          =						tokens + [tokenizer.unk_token]
            __snake_case			:Optional[int]          =						[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
            __snake_case			:List[Any]          =						tokenizer.convert_tokens_to_ids(a__    )
            self.assertListEqual(a__			,						a__    )



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            __snake_case			:Tuple          =						self.get_tokenizer()

            # Testing tokenization
            __snake_case			:Optional[Any]          =						"""こんにちは、<|bagoftoken|>世界。こんばんは、<|bagoftoken|>㔺界。"""
            __snake_case			:Tuple          =						"""こんにちは、、、、世界。こんばんは、、、、世界。"""
            __snake_case			:str          =						tokenizer.encode(a__    )
            __snake_case			:Optional[int]          =						tokenizer.decode(a__    )
            self.assertEqual(a__			,						a__    )



       @slow
       def 					__lowercase						(							self    ) ->      List[Any]:






            '''simple docstring'''





            __snake_case			:List[str]          =						self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese"""    )

            # Testing tokenization
            __snake_case			:str          =						"""こんにちは、世界。"""
            __snake_case			:List[str]          =						"""こんばんは、㔺界。😀"""
            __snake_case			:Dict          =						"""こんにちは、世界。こんばんは、世界。😀"""
            __snake_case			:int          =						tokenizer.encode(prefix_text + input_text    )
            __snake_case			:Optional[Any]          =						tokenizer.encode(""""""			,						prefix_text=prefix_text + input_text    )
            __snake_case			:Any          =						tokenizer.encode(a__			,						prefix_text=a__    )
            __snake_case			:Optional[Any]          =						tokenizer.decode(a__    )
            __snake_case			:Optional[Any]          =						tokenizer.decode(a__    )
            __snake_case			:Optional[Any]          =						tokenizer.decode(a__    )
            self.assertEqual(a__			,						a__    )
            self.assertEqual(a__			,						a__    )
            self.assertEqual(a__			,						a__    )



       @slow
       def 					__lowercase						(							self    ) ->      Any:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese"""    )

            # Testing tokenization
            __snake_case			:List[Any]          =						"""こんにちは、世界。"""
            __snake_case			:Dict          =						"""こんばんは、㔺界。😀"""

            __snake_case			:Optional[int]          =						len(tokenizer.encode(a__    )    ) - 2
            __snake_case			:Union[str, Any]          =						len(tokenizer.encode(a__    )    ) - 2

            __snake_case			:Union[str, Any]          =						[1] + [0] * (len_prefix + len_text + 1)
            __snake_case			:Optional[Any]          =						[1] * (len_prefix + len_text + 1) + [0]
            __snake_case			:Union[str, Any]          =						[1] + [1] * (len_prefix) + [0] * (len_text + 1)

            __snake_case			:int          =						tokenizer(prefix_text + input_text    ).token_type_ids
            __snake_case			:List[Any]          =						tokenizer(""""""			,						prefix_text=prefix_text + input_text    ).token_type_ids
            __snake_case			:int          =						tokenizer(a__			,						prefix_text=a__    ).token_type_ids
            self.assertListEqual(a__			,						a__    )
            self.assertListEqual(a__			,						a__    )
            self.assertListEqual(a__			,						a__    )



       @slow
       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            __snake_case			:Optional[Any]          =						self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese"""    )

            __snake_case			:Tuple          =						tokenizer.encode("""あンいワ"""    )
            __snake_case			:List[str]          =						tokenizer.encode(""""""			,						prefix_text="""あンいワ"""    )
            __snake_case			:int          =						tokenizer.encode("""いワ"""			,						prefix_text="""あン"""    )

            self.assertEqual(tokenizer.decode(a__    )			,						tokenizer.decode(a__    )    )
            self.assertEqual(tokenizer.decode(a__    )			,						tokenizer.decode(a__    )    )
            self.assertNotEqual(a__			,						a__    )
            self.assertNotEqual(a__			,						a__    )
            self.assertEqual(x_token_a[1]			,						x_token_a[-1]    )  # SEG token
            self.assertEqual(x_token_a[1]			,						x_token_a[3]    )  # SEG token



       @slow
       def 					__lowercase						(							self    ) ->      Union[str, Any]:






            '''simple docstring'''





            __snake_case			:List[str]          =						self.tokenizer_class.from_pretrained("""Tanrei/GPTSAN-japanese"""    )

            __snake_case			:int          =						[["""武田信玄""", """は、"""], ["""織田信長""", """の配下の、"""]]
            __snake_case			:Optional[int]          =						tokenizer(a__			,						padding=a__    )
            __snake_case			:int          =						tokenizer.batch_encode_plus(a__			,						padding=a__    )

            # fmt: off
            __snake_case			:Tuple          =						[[3_59_93, 86_40, 2_59_48, 3_59_98, 3_06_47, 3_56_75, 3_59_99, 3_59_99], [3_59_93, 1_03_82, 98_68, 3_59_98, 3_06_46, 94_59, 3_06_46, 3_56_75]]
            __snake_case			:Any          =						[[1, 1, 1, 0, 0, 0, 0, 0], [1, 1, 1, 0, 0, 0, 0, 0]]
            __snake_case			:int          =						[[1, 1, 1, 1, 1, 1, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1]]
            # fmt: on
            self.assertListEqual(x_token.input_ids			,						a__    )
            self.assertListEqual(x_token.token_type_ids			,						a__    )
            self.assertListEqual(x_token.attention_mask			,						a__    )
            self.assertListEqual(x_token_a.input_ids			,						a__    )
            self.assertListEqual(x_token_a.token_type_ids			,						a__    )
            self.assertListEqual(x_token_a.attention_mask			,						a__    )



       def 					__lowercase						(							self    ) ->      List[str]:






            '''simple docstring'''





            pass



       def 					__lowercase						(							self    ) ->      Optional[int]:






            '''simple docstring'''





            pass


