




import unittest

import numpy as np

from transformers import (
    GPTSanJapaneseConfig,
    GPTSanJapaneseForConditionalGeneration,
    GPTSanJapaneseModel,
    GPTSanJapaneseTokenizer,
    is_torch_available,
)
from transformers.generation import GenerationConfig
from transformers.testing_utils import require_torch, slow, tooslow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin







class 		__UpperCAmelCase:





     """simple docstring"""



     def __init__(      self :       List[str]						,				A :       Any						,				A :       Union[str, Any]=3_60_00						,				A :       List[str]=13						,				A :       Tuple=7						,				A :       Optional[int]=True						,				A :       Union[str, Any]=32						,				A :       List[Any]=42						,				A :       Dict=5						,				A :       List[str]=2						,				A :       Tuple=4						,				A :       List[str]=2						,				A :       Optional[Any]=32						,				A :       Dict=80						,				A :       Tuple=33						,				A :       Any=0.0						,				A :       Optional[Any]=1E-6						,				A :       Optional[int]=1_00						,				A :       Tuple=0.0						,				)			->			Dict:






         """simple docstring"""
         a__      =       vocab_size
         a__      =       parent
         a__      =       batch_size
         a__      =       num_contexts
         # For common tests
         a__      =       self.num_contexts
         a__      =       is_training
         a__      =       hidden_size
         a__      =       num_ext_layers
         a__      =       ext_size
         a__      =       num_hidden_layers
         a__      =       num_attention_heads
         a__      =       num_experts
         a__      =       d_ff
         a__      =       d_ext
         a__      =       d_spout
         a__      =       dropout_rate
         a__      =       layer_norm_epsilon
         a__      =       expert_capacity
         a__      =       router_jitter_noise

     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			int:






         """simple docstring"""
         return GPTSanJapaneseConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			Optional[Any]:






         """simple docstring"""
         a__      =       ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size    )

         a__      =       self.get_config()

         return (config, input_ids)

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			int:






         """simple docstring"""
         a__      =       ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size    )

         a__      =       self.get_config()

         return (config, {"input_ids": input_ids})

     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			List[str]:






         """simple docstring"""
         return GPTSanJapaneseConfig(
             vocab_size=self.vocab_size						,				num_contexts=self.seq_length						,				d_model=self.hidden_size						,				d_ff=self.d_ff						,				d_ext=self.d_ext						,				d_spout=self.d_spout						,				num_switch_layers=self.num_hidden_layers - self.num_ext_layers						,				num_ext_layers=self.num_ext_layers						,				num_heads=self.num_attention_heads						,				num_experts=self.num_experts						,				expert_capacity=self.expert_capacity						,				dropout_rate=self.dropout_rate						,				layer_norm_epsilon=self.layer_norm_epsilon						,				router_jitter_noise=self.router_jitter_noise						,				)






     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]						,				A :       Dict						,				A :       int						,				)			->			Optional[int]:






         """simple docstring"""
         a__      =       GPTSanJapaneseForConditionalGeneration(config=A    )
         model.to(A    )
         model.eval()
         a__      =       model(
             input_ids=A						,				)
         self.parent.assertIsNotNone(A    )







@require_torch
class 		__UpperCAmelCase(		lowercase__       ,	lowercase__       ,	unittest.TestCase ):





     """simple docstring"""



     __UpperCAmelCase	: Any						=    (GPTSanJapaneseModel,) if is_torch_available() else ()
     __UpperCAmelCase	: List[str]						=    (
         {
             """conversational""": GPTSanJapaneseForConditionalGeneration,
             """feature-extraction""": GPTSanJapaneseForConditionalGeneration,
             """summarization""": GPTSanJapaneseForConditionalGeneration,
             """text2text-generation""": GPTSanJapaneseForConditionalGeneration,
             """translation""": GPTSanJapaneseForConditionalGeneration,
         }
         if is_torch_available()
         else {}
     )
     __UpperCAmelCase	: str						=    False
     __UpperCAmelCase	: Any						=    False
     __UpperCAmelCase	: Optional[Any]						=    False
     __UpperCAmelCase	: Optional[Any]						=    False
     __UpperCAmelCase	: Any						=    False
     __UpperCAmelCase	: Tuple						=    False
     __UpperCAmelCase	: Optional[int]						=    False
     __UpperCAmelCase	: List[str]						=    False
     # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
     __UpperCAmelCase	: List[str]						=    [0.8, 0.9]

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]						,				A :       Tuple						,				A :       Tuple						,				A :       int						,				A :       Union[str, Any]						,				A :       Dict    )			->			str:






         """simple docstring"""
         if pipeline_test_casse_name == "SummarizationPipelineTests":
             # TODO: fix `_reorder_cache` is not implemented for this model
             return True
         elif pipeline_test_casse_name == "Text2TextGenerationPipelineTests":
             # TODO: check this.
             return True

         return False

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			List[str]:






         """simple docstring"""
         a__      =       GPTSanJapaneseTester(self    )
         a__      =       ConfigTester(self						,				config_class=A						,				d_model=37    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			int:






         """simple docstring"""
         GPTSanJapaneseConfig()

     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			List[str]:






         """simple docstring"""
         a__      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A    )

     @unittest.skip(
         reason="""skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"""    )
     def 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			Any:






         """simple docstring"""
         super().test_model_parallelism()






     @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""    )
     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			List[str]:






         """simple docstring"""
         pass







@require_torch
class 		__UpperCAmelCase(		lowercase__       ,	lowercase__       ,	unittest.TestCase ):





     """simple docstring"""



     __UpperCAmelCase	: List[str]						=    (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
     __UpperCAmelCase	: List[Any]						=    False
     __UpperCAmelCase	: Any						=    False
     __UpperCAmelCase	: List[str]						=    False
     __UpperCAmelCase	: Union[str, Any]						=    False
     __UpperCAmelCase	: List[Any]						=    False
     __UpperCAmelCase	: int						=    False
     # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
     __UpperCAmelCase	: Optional[int]						=    [0.8, 0.9]

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			Optional[int]:






         """simple docstring"""
         a__      =       GPTSanJapaneseTester(self    )
         a__      =       ConfigTester(self						,				config_class=A						,				d_model=37    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			Tuple:






         """simple docstring"""
         GPTSanJapaneseConfig()

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			int:






         """simple docstring"""
         a__      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A    )

     @unittest.skip(
         reason="""skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"""    )
     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			int:






         """simple docstring"""
         super().test_model_parallelism()

     @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""    )
     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			List[str]:






         """simple docstring"""
         pass

     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			Tuple:






         """simple docstring"""
         a__      =       GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""    )
         a__      =       GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""    )
         a__      =       tokenizer.encode("""武田信玄は"""						,				return_tensors="""pt"""    )
         a__      =       model(A    )
         a__      =       outputs.logits.detach().cpu().numpy()
         # Output of original model created with mesh-tensoflow
         a__      =       [
             # fmt: off
             [-1_2.0_3_7_8_3_9_8_8_9_5_2_6_3_6_7, -1_2.4_3_3_0_6_1_5_9_9_7_3_1_4_4_5, -1_4.3_3_3_8_4_0_3_7_0_1_7_8_2_2_3, -1_2.4_5_0_3_4_5_9_9_3_0_4_1_9_9_2, -1_1.1_6_6_1_3_7_6_9_5_3_1_2_5,
             -1_1.9_3_0_1_3_7_6_3_4_2_7_7_3_4_4, -1_0.6_5_9_7_4_0_4_4_7_9_9_8_0_4_7, -1_2.9_0_9_5_7_4_5_0_8_6_6_6_9_9_2, -1_3.2_4_1_0_4_3_0_9_0_8_2_0_3_1_2, -1_3.3_9_8_5_7_9_5_9_7_4_7_3_1_4_5,
             -1_1.1_0_7_5_2_4_8_7_1_8_2_6_1_7_2, -1_2.3_6_8_5_9_4_1_6_9_6_1_6_7, -2_2.9_7_9_4_3_1_1_5_2_3_4_3_7_5, -1_0.4_8_1_0_6_7_6_5_7_4_7_0_7_0_3, -1_2.4_8_4_0_3_0_7_2_3_5_7_1_7_7_7,
             -1_2.8_0_7_3_6_0_6_4_9_1_0_8_8_8_7, -1_4.7_6_9_7_0_0_0_5_0_3_5_4_0_0_4, -1_2.2_3_3_5_7_9_6_3_5_6_2_0_1_1_7, -1_3.4_2_8_1_4_5_4_0_8_6_3_0_3_7_1, -2_2.6_2_4_1_7_7_9_3_2_7_3_9_2_5_8],
             [-7.5_1_1_1_4_9_8_8_3_2_7_0_2_6_4, -8.2_8_1_8_5_1_7_6_8_4_9_3_6_5_2, -7.9_4_3_1_2_7_1_5_5_3_0_3_9_5_5, -7.5_5_0_2_1_3_3_3_6_9_4_4_5_8, -6.4_9_8_6_9_9_6_6_5_0_6_9_5_8,
             -7.5_8_6_7_9_6_2_8_3_7_2_1_9_2_4, -6.9_7_8_0_8_5_9_9_4_7_2_0_4_5_9, -7.8_3_9_1_4_5_1_8_3_5_6_3_2_3_2, -8.2_1_9_6_4_1_6_8_5_4_8_5_8_4, -8.6_9_5_0_9_1_2_4_7_5_5_8_5_9_4,
             -6.7_0_6_9_1_0_6_1_0_1_9_8_9_7_5, -6.6_5_8_5_7_9_8_2_6_3_5_4_9_8_0_5, -1_9.5_6_5_6_9_8_6_2_3_6_5_7_2_2_7, -5.3_5_3_8_4_2_7_3_5_2_9_0_5_2_7, -8.3_5_0_6_8_6_0_7_3_3_0_3_2_2_3,
             -8.0_3_9_3_8_8_6_5_6_6_1_6_2_1_1, -1_0.8_5_6_5_6_9_2_9_0_1_6_1_1_3_3, -7.7_5_1_5_4_4_4_7_5_5_5_5_4_2, -8.8_1_9_0_2_2_1_7_8_6_4_9_9_0_2, -1_9.5_1_5_3_2_7_4_5_3_6_1_3_2_8],
             [-9.7_3_0_6_6_2_3_4_5_8_8_6_2_3, -1_0.2_2_3_9_2_2_7_2_9_4_9_2_1_8_8, -9.9_3_2_9_8_1_4_9_1_0_8_8_8_6_7, -1_1.8_5_7_8_3_6_7_2_3_3_2_7_6_3_7, -7.6_6_2_6_2_6_2_6_6_4_7_9_4_9_2,
             -1_1.1_3_5_2_9_1_0_9_9_5_4_8_3_4, -7.7_6_5_0_9_7_6_1_8_1_0_3_0_2_7, -1_1.4_7_2_9_2_3_2_7_8_8_0_8_5_9_4, -9.5_4_3_1_4_9_9_4_8_1_2_0_1_1_7, -1_1.9_0_5_6_3_3_9_2_6_3_9_1_6_0_2,
             -9.3_6_6_1_6_4_2_0_7_4_5_8_4_9_6, -1_1.5_7_3_4_2_8_1_5_3_9_9_1_7, -2_3.6_9_9_0_0_3_2_1_9_6_0_4_4_9_2, -9.4_2_9_5_9_0_2_2_5_2_1_9_7_2_7, -1_0.4_2_8_3_9_2_4_1_0_2_7_8_3_2,
             -1_0.5_8_5_2_4_0_3_6_4_0_7_4_7_0_7, -1_0.9_4_7_7_1_9_5_7_3_9_7_4_6_1, -1_1.0_9_5_4_1_6_0_6_9_0_3_0_7_6_2, -1_0.3_9_0_2_4_0_6_6_9_2_5_0_4_8_8, -2_3.7_6_9_3_7_2_9_4_0_0_6_3_4_7_7],
             [-9.7_2_8_2_6_5_7_6_2_3_2_9_1_0_2, -9.8_5_9_7_1_2_6_0_0_7_0_8_0_0_8, -1_0.0_9_7_2_9_2_9_0_0_0_8_5_4_5, -9.6_7_8_5_2_2_1_0_9_9_8_5_3_5_2, -6.8_7_9_5_1_9_9_3_9_4_2_2_6_0_7,
             -9.6_8_4_8_7_5_4_8_8_2_8_1_2_5, -4.2_8_0_3_4_2_5_7_8_8_8_7_9_3_9_5, -1_0.0_1_8_9_1_4_2_2_2_7_1_7_2_8_5, -9.3_0_8_4_4_5_9_3_0_4_8_0_9_5_7, -1_0.6_3_3_9_4_5_4_6_5_0_8_7_8_9,
             -8.0_8_3_6_4_6_7_7_4_2_9_1_9_9_2, -9.0_6_3_0_1_4_9_8_4_1_3_0_8_6, -2_1.9_0_4_2_6_6_3_5_7_4_2_1_8_7_5, -8.9_0_1_6_0_8_4_6_7_1_0_2_0_5, -8.8_4_1_8_7_6_0_2_9_9_6_8_2_6_2,
             -1_1.8_5_6_7_1_9_9_7_0_7_0_3_1_2_5, -1_2.0_7_9_3_9_8_1_5_5_2_1_2_4_0_2, -1_1.2_3_3_7_5_3_2_0_4_3_4_5_7_0_3, -1_0.1_7_7_3_3_8_6_0_0_1_5_8_6_9_1, -2_1.8_7_2_5_6_6_2_2_3_1_4_4_5_3],
             [-9.6_6_9_7_6_4_5_1_8_7_3_7_7_9_3, -9.6_1_4_1_9_8_6_8_4_6_9_2_3_8_3, -9.8_1_4_5_1_0_3_4_5_4_5_8_9_8_4, -9.9_9_6_5_0_1_9_2_2_6_0_7_4_2_2, -1_1.3_7_5_6_9_0_4_6_0_2_0_5_0_7_8,
             -1_0.1_1_3_4_0_5_2_2_7_6_6_1_1_3_3, -1_0.5_4_6_8_6_7_3_7_0_6_0_5_4_6_9, -1_0.0_4_3_6_9_0_6_8_1_4_5_7_5_2, -1_0.9_0_7_8_0_9_2_5_7_5_0_7_3_2_4, -1_0.5_0_4_2_1_6_1_9_4_1_5_2_8_3_2,
             -1_1.1_2_9_1_9_9_0_2_8_0_1_5_1_3_7, -1_0.1_5_1_1_2_4_0_0_0_5_4_9_3_1_6, -2_1.9_6_5_8_6_7_9_9_6_2_1_5_8_2, -9.0_8_6_3_4_9_4_8_7_3_0_4_6_8_8, -1_1.7_3_0_3_3_9_0_5_0_2_9_2_9_6_9,
             -1_0.4_6_0_6_6_7_6_1_0_1_6_8_4_5_7, -1_0.2_9_8_0_4_9_9_2_6_7_5_7_8_1_2, -1_0.7_8_4_1_4_8_2_1_6_2_4_7_5_5_9, -1_0.8_4_0_6_9_3_4_7_3_8_1_5_9_1_8, -2_2.0_3_1_5_2_8_4_7_2_9_0_0_3_9],
             # fmt: on
         ]
         a__      =       np.array(A    ).flatten()
         a__      =       output_logits[0, :, :20].flatten()

         def check(A :       Optional[Any]						,				A :       List[str]						,				A :       List[Any]=5E-4    ):
             return abs(a - b    ) < epsilon * max(abs(A    )						,				abs(A    )    )

         self.assertTrue(np.all([check(target[i]						,				predict[i]    ) for i in range(len(A    )    )]    )    )

     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			str:






         """simple docstring"""
         a__      =       GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""    )
         a__      =       GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""    )
         model.to(A    )

         # set deterministically
         a__      =       GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""    )
         a__      =       1

         # use different length sentences to test batching
         a__      =       [
             """甲斐なら武田と言うほど""",
             """織田信長は、""",
         ]

         a__      =       """left"""
         a__      =       tokenizer(A						,				return_tensors="""pt"""						,				padding=A    )
         a__      =       inputs["""input_ids"""].to(A    )

         self.assertNotEqual(inputs["""attention_mask"""][0].numpy().tolist()						,				inputs["""attention_mask"""][1].numpy().tolist()    )

         a__      =       model.generate(
             input_ids=A						,				attention_mask=inputs["""attention_mask"""].to(A    )						,				max_new_tokens=3						,				generation_config=A						,				)

         a__      =       tokenizer(sentences[0]						,				return_tensors="""pt"""    ).input_ids.to(A    )
         a__      =       model.generate(
             input_ids=A						,				max_new_tokens=3						,				generation_config=A    )

         a__      =       tokenizer(sentences[1]						,				return_tensors="""pt"""    ).input_ids.to(A    )
         a__      =       model.generate(input_ids=A						,				max_new_tokens=3						,				generation_config=A    )

         self.assertNotEqual(inputs_non_padded.shape						,				inputs_padded.shape    )

         a__      =       tokenizer.batch_decode(A						,				skip_special_tokens=A    )
         a__      =       tokenizer.decode(output_non_padded[0]						,				skip_special_tokens=A    )
         a__      =       tokenizer.decode(output_padded[0]						,				skip_special_tokens=A    )

         a__      =       [
             """甲斐なら武田と言うほど甲斐の武田""",
             """織田信長は、このような""",
         ]
         self.assertListEqual(A						,				A    )
         self.assertListEqual(A						,				[non_padded_sentence, padded_sentence]    )

     @tooslow
     def 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			Any:






         """simple docstring"""
         a__      =       GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""    )
         a__      =       GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""    )
         # Output of original model created with mesh-tensoflow
         a__      =       [
             ("""武田信玄は""", 3_56_75),
             ("""武田信玄は、""", 45),
             ("""武田信玄は、この""", 29),
             ("""武田信玄は、このよう""", 3_06_42),
             ("""武田信玄は、このような""", 3_56_80),
             ("""武田信玄は、このような「""", 86_40),
             ("""武田信玄は、このような「武田""", 3_16_17),
             ("""武田信玄は、このような「武田家""", 3_06_46),
             ("""武田信玄は、このような「武田家の""", 3_16_17),
             ("""武田信玄は、このような「武田家の家""", 3_13_81),
         ]
         for input, output in target:
             a__      =       tokenizer.encode(A						,				return_tensors="""pt"""    )
             a__      =       model(A    )
             a__      =       outputs.logits.detach().cpu().numpy()[0]
             a__      =       np.argmax(output_logits[-1]    )
             self.assertEqual(A						,				A    )

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






         """simple docstring"""
         a__      =       GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""    )
         a__      =       GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""    )
         model.to(A    )

         # set deterministically
         a__      =       GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""    )
         a__      =       1

         a__      =       """武田信玄は、"""
         a__      =       tokenizer(A						,				return_tensors="""pt"""    ).input_ids.to(A    )
         a__      =       tokenizer([input_text, input_text]						,				return_tensors="""pt"""    ).input_ids.to(A    )

         # spout from uniform and one-hot
         a__      =       [
             # fmt: off
             [0.8_7_8_8_2_2_0_8, 0.3_8_4_2_6_3_9_6, 0.3_3_2_2_0_2_4_8, 0.4_3_8_9_0_4_0_6, 0.1_6_5_6_2_2_5_2,
             0.0_4_8_0_3_9_8_5, 0.2_1_1_5_7_2  , 0.2_3_1_8_8_4_7_3, 0.3_7_1_5_3_0_6_8, 0.7_8_3_6_3_7_7 ,
             0.0_2_1_6_0_1_7_2, 0.3_8_7_6_1_7_1_9, 0.7_5_2_9_0_7_7_2, 0.9_0_1_9_8_8_5_7, 0.3_4_3_6_5_7_7_7,
             0.6_4_1_6_8_1_6_9, 0.4_4_3_1_8_4_7_1, 0.1_4_5_7_5_7_4_6, 0.9_2_5_6_2_8_8_1, 0.4_0_8_1_2_1_4_8,
             0.2_9_0_1_9_1_2_2, 0.8_8_8_6_1_5_9_9, 0.6_5_5_2_4_8_4_6, 0.4_3_5_6_3_4_5_6, 0.3_8_1_7_7_1_8_7,
             0.7_0_8_3_2_9_6_5, 0.8_1_5_2_7_8_9_2, 0.6_8_8_3_2_8_1_2, 0.3_8_8_3_3_1_9_2, 0.4_5_6_1_5_2_2 ,
             0.1_4_8_2_8_8_1_7, 0.4_7_2_4_8_2_1_3, 0.5_4_3_5_7_3_3_5, 0.8_2_0_0_9_5_6_6, 0.1_3_3_8_8_8_4 ,
             0.0_2_7_5_5_4_1_7, 0.1_9_7_6_4_6_7_7, 0.2_4_2_2_0_8_4 , 0.0_4_7_5_7_6_7_4, 0.6_5_4_0_9_6_0_6,
             0.0_8_2_4_5_8_9 , 0.0_3_3_0_4_3_8_3, 0.9_4_3_8_7_6_8_9, 0.9_8_7_6_4_5_0_9, 0.8_2_4_3_3_9_0_1,
             0.2_7_6_4_6_7_4_1, 0.6_4_9_0_7_4_9_3, 0.7_6_0_0_9_4_0_6, 0.3_0_0_8_7_9_1_5, 0.1_7_9_0_4_6_8_9,
             0.4_1_6_0_1_7_1_4, 0.6_7_0_4_6_3_9_8, 0.1_0_4_2_2_8_2_2, 0.0_8_4_4_7_3_7_4, 0.0_7_3_5_4_3_4_4,
             0.6_1_4_2_3_5_6_5, 0.7_0_2_8_4_8_6_6, 0.7_5_3_2_3_3_3 , 0.1_9_7_2_0_3_8 , 0.2_9_5_7_5_6_5_9,
             0.9_0_5_8_3_8_8_6, 0.2_9_2_6_5_3_0_7, 0.5_0_0_0_0_1_7_5, 0.7_0_4_0_7_6_5_5, 0.8_8_9_3_6_3  ,
             0.8_1_9_0_4_4_1_8, 0.6_6_8_2_9_1_2_8, 0.6_4_4_6_8_8_1_5, 0.5_6_5_6_3_7_2_3, 0.8_5_6_0_1_8_7_5,
             0.9_4_9_2_4_6_7_2, 0.0_0_1_6_6_7_6_2, 0.2_5_2_2_0_6_4_3, 0.7_4_5_4_0_2_1_9, 0.6_7_9_9_3_2_4_7,
             0.1_5_4_9_6_7_5 , 0.3_9_3_8_5_3_5_2, 0.9_2_1_5_3_6_0_7, 0.6_3_7_4_5_9_3_1, 0.2_7_7_5_9_0_4_3,
             0.8_4_7_0_2_2_9_5, 0.6_5_9_0_4_2_7_1, 0.5_8_6_7_6_6_1_4, 0.8_6_6_6_9_3_6 , 0.3_9_6_0_7_4_3_8,
             0.7_9_9_5_4_9_8_3, 0.4_2_2_2_0_6_9_7, 0.3_9_6_5_0_3_8_1, 0.7_8_4_9_8_6_4 , 0.5_6_1_5_0_2_0_1,
             0.1_5_6_7_8_9_2_5, 0.1_4_7_4_6_0_3_2, 0.3_4_5_4_2_1_1_4, 0.4_7_0_2_6_7_8_3, 0.1_1_9_5_6_4_8_9,
             0.2_5_4_2_1_4_3_5, 0.3_3_7_8_8_9_0_1, 0.6_8_9_3_4_8_4_2, 0.3_6_4_2_4_6_8_5, 0.7_1_7_3_7_8_9_8,
             0.3_8_9_8_3_4_4_9, 0.9_4_3_9_3_7_7_9, 0.3_9_5_7_5_5_8_8, 0.3_6_6_1_6_5_5_3, 0.8_7_1_0_4_6_6_5,
             0.6_4_6_3_0_2_0_3, 0.2_2_5_1_6_9_0_5, 0.8_8_2_7_0_8_0_4, 0.1_5_0_3_1_3_3_8, 0.7_5_1_4_4_3_4_5,
             0.4_6_4_5_9_0_2_5, 0.8_5_3_9_6_4_5_4, 0.8_6_3_5_5_6_4_3, 0.6_5_1_3_9_8_5_1, 0.7_0_2_6_6_0_6_1,
             0.3_0_2_4_1_3_8_9, 0.8_1_0_5_6_4_9_7, 0.8_8_8_6_5_9_6_9, 0.3_8_7_7_3_8_0_7, 0.7_0_6_3_5_8_4_9,
             0.9_0_7_1_8_4_5_9, 0.4_3_2_4_5_7_8_9, 0.2_8_0_0_0_6_5_4, 0.4_5_9_3_5_5_6_2, 0.0_8_7_7_3_5_1_9,
             0.9_5_5_2_1_5_1 , 0.9_3_9_0_1_5_1_1, 0.2_2_4_8_9_2_8_8], # uniform
             [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
              0., 0., 0., 0., 0., 0., 0., 0.],
             # fmt: on
         ]

         a__      =       model.generate(
             input_ids=A						,				spout=spouts[0]						,				max_new_tokens=20						,				generation_config=A						,				)

         a__      =       model.generate(
             input_ids=A						,				spout=spouts[1]						,				max_new_tokens=20						,				generation_config=A						,				)

         a__      =       model.generate(
             input_ids=A						,				spout=A						,				max_new_tokens=20						,				generation_config=A						,				)

         a__      =       tokenizer.decode(outputa[0]    )
         a__      =       tokenizer.decode(outputa[0]    )
         a__      =       tokenizer.batch_decode(A    )

         a__      =       [
             """武田信玄は、武田氏の滅亡後、武田氏の居城であった甲斐武田氏の居城である""",
             """武田信玄は、武田家の滅亡を防ぐため、武田家の家臣である武田信虎を討""",
         ]
         self.assertListEqual(A						,				A    )
         self.assertListEqual(A						,				[outa_sentence, outa_sentence]    )






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






         """simple docstring"""
         a__      =       GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""    )
         a__      =       GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""    )
         model.to(A    )

         # set deterministically
         a__      =       GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""    )
         a__      =       1

         a__      =       """武田信玄"""
         a__      =       """織田信長"""
         a__      =       """は、"""
         a__      =       """が、"""
         a__      =       tokenizer(A						,				prefix_text=A						,				return_tensors="""pt"""    )
         a__      =       tokenizer(A						,				prefix_text=A						,				return_tensors="""pt"""    )
         a__      =       tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]						,				return_tensors="""pt"""    )

         a__      =       model.generate(
             input_ids=input_tok_a.input_ids.to(A    )						,				token_type_ids=input_tok_a.token_type_ids.to(A    )						,				max_new_tokens=20						,				generation_config=A						,				)

         a__      =       model.generate(
             input_ids=input_tok_a.input_ids.to(A    )						,				token_type_ids=input_tok_a.token_type_ids.to(A    )						,				max_new_tokens=20						,				generation_config=A						,				)

         a__      =       model.generate(
             input_ids=input_tok_a.input_ids.to(A    )						,				token_type_ids=input_tok_a.token_type_ids.to(A    )						,				attention_mask=input_tok_a.attention_mask.to(A    )						,				max_new_tokens=20						,				generation_config=A						,				)

         a__      =       tokenizer.decode(outputa[0]    )
         a__      =       tokenizer.decode(outputa[0]    )
         a__      =       tokenizer.batch_decode(A    )

         a__      =       [
             """武田信玄は、武田氏の祖である武田信虎を、その子・武田信友を擁して""",
             """織田信長が、織田信長の妻・お市の方を妻として迎えたという逸話が残""",
         ]
         self.assertListEqual(A						,				A    )
         self.assertListEqual(A						,				[outa_sentence, outa_sentence]    )












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 import torch

 from transformers import CamembertModel







@require_torch
@require_sentencepiece
@require_tokenizers
class 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



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






         """simple docstring"""
         a__      =       CamembertModel.from_pretrained("""camembert-base"""    )
         model.to(A    )

         a__      =       torch.tensor(
             [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]						,				device=A						,				dtype=torch.long						,				)  # J'aime le camembert !
         with torch.no_grad():
             a__      =       model(A    )["""last_hidden_state"""]
         a__      =       torch.Size((1, 10, 7_68)    )
         self.assertEqual(output.shape						,				A    )
         # compare the actual values for a slice.
         a__      =       torch.tensor(
             [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,				device=A						,				dtype=torch.float						,				)
         # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
         # camembert.eval()
         # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

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






