

"""simple docstring"""




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					:


		def __init__(self					:						Tuple	,	__SCREAMING_SNAKE_CASE					:						str	,	__SCREAMING_SNAKE_CASE					:						str=3_6_0_0_0	,	__SCREAMING_SNAKE_CASE					:						Tuple=1_3	,	__SCREAMING_SNAKE_CASE					:						str=7	,	__SCREAMING_SNAKE_CASE					:						Dict=True	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]=3_2	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=4_2	,	__SCREAMING_SNAKE_CASE					:						str=5	,	__SCREAMING_SNAKE_CASE					:						Dict=2	,	__SCREAMING_SNAKE_CASE					:						List[Any]=4	,	__SCREAMING_SNAKE_CASE					:						str=2	,	__SCREAMING_SNAKE_CASE					:						Tuple=3_2	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]=8_0	,	__SCREAMING_SNAKE_CASE					:						str=3_3	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=0.0	,	__SCREAMING_SNAKE_CASE					:						Optional[int]=1E-6	,	__SCREAMING_SNAKE_CASE					:						int=1_0_0	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=0.0	,	):
							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):
							return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese")


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							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					:						List[Any]):
							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					:						str):
							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					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						Dict	,	):
							A     =   GPTSanJapaneseForConditionalGeneration(config=__SCREAMING_SNAKE_CASE)
							model.to(__SCREAMING_SNAKE_CASE)
							model.eval()
							A     =   model(
							    input_ids=__SCREAMING_SNAKE_CASE	,	)
							self.parent.assertIsNotNone(__SCREAMING_SNAKE_CASE)





@require_torch
class      __UpperCamelCase					(  _A	,						_A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       (GPTSanJapaneseModel,) if is_torch_available() else ()
		SCREAMING_SNAKE_CASE														=       (
		    {
		        "conversational": GPTSanJapaneseForConditionalGeneration,
		        "feature-extraction": GPTSanJapaneseForConditionalGeneration,
		        "summarization": GPTSanJapaneseForConditionalGeneration,
		        "text2text-generation": GPTSanJapaneseForConditionalGeneration,
		        "translation": GPTSanJapaneseForConditionalGeneration,
		    }
		    if is_torch_available()
		    else {}
		)
		SCREAMING_SNAKE_CASE														=       False
		SCREAMING_SNAKE_CASE														=       False
		SCREAMING_SNAKE_CASE														=       False
		SCREAMING_SNAKE_CASE														=       False
		SCREAMING_SNAKE_CASE														=       False
		SCREAMING_SNAKE_CASE														=       False
		SCREAMING_SNAKE_CASE														=       False
		SCREAMING_SNAKE_CASE														=       False
		# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
		SCREAMING_SNAKE_CASE														=       [0.8, 0.9]


		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						List[str]	,	__SCREAMING_SNAKE_CASE					:						Dict	,	__SCREAMING_SNAKE_CASE					:						str	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]):
							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[Any]):
							A     =   GPTSanJapaneseTester(self)
							A     =   ConfigTester(self	,	config_class=__SCREAMING_SNAKE_CASE	,	d_model=3_7)


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							GPTSanJapaneseConfig()


		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							A     =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)


		@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):
							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					:						Tuple):
							pass





@require_torch
class      __UpperCamelCase					(  _A	,						_A	,						unittest.TestCase		):
		SCREAMING_SNAKE_CASE														=       (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
		SCREAMING_SNAKE_CASE														=       False
		SCREAMING_SNAKE_CASE														=       False
		SCREAMING_SNAKE_CASE														=       False
		SCREAMING_SNAKE_CASE														=       False
		SCREAMING_SNAKE_CASE														=       False
		SCREAMING_SNAKE_CASE														=       False
		# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
		SCREAMING_SNAKE_CASE														=       [0.8, 0.9]


		def    SCREAMING_SNAKE_CASE__  (self					:						int):
							A     =   GPTSanJapaneseTester(self)
							A     =   ConfigTester(self	,	config_class=__SCREAMING_SNAKE_CASE	,	d_model=3_7)


		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							GPTSanJapaneseConfig()


		def    SCREAMING_SNAKE_CASE__  (self					:						Tuple):
							A     =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)


		@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):
							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					:						List[Any]):
							pass


		@slow
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[Any]):
							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(__SCREAMING_SNAKE_CASE)
							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(__SCREAMING_SNAKE_CASE).flatten()
							A     =   output_logits[0, :, :2_0].flatten()

							def check(__SCREAMING_SNAKE_CASE					:						Union[str, Any]	,	__SCREAMING_SNAKE_CASE					:						str	,	__SCREAMING_SNAKE_CASE					:						int=5E-4):
												return abs(a - b) < epsilon * max(abs(__SCREAMING_SNAKE_CASE)	,	abs(__SCREAMING_SNAKE_CASE))

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


		@slow
		def    SCREAMING_SNAKE_CASE__  (self					:						List[Any]):
							A     =   GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese")
							A     =   GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
							model.to(__SCREAMING_SNAKE_CASE)

							# 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(__SCREAMING_SNAKE_CASE	,	return_tensors="pt"	,	padding=__SCREAMING_SNAKE_CASE)
							A     =   inputs["input_ids"].to(__SCREAMING_SNAKE_CASE)

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

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

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

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

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

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

							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(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)
							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	[non_padded_sentence, padded_sentence])


		@tooslow
		def    SCREAMING_SNAKE_CASE__  (self					:						Dict):
							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_5_6_7_5),
							    ("武田信玄は、", 4_5),
							    ("武田信玄は、この", 2_9),
							    ("武田信玄は、このよう", 3_0_6_4_2),
							    ("武田信玄は、このような", 3_5_6_8_0),
							    ("武田信玄は、このような「", 8_6_4_0),
							    ("武田信玄は、このような「武田", 3_1_6_1_7),
							    ("武田信玄は、このような「武田家", 3_0_6_4_6),
							    ("武田信玄は、このような「武田家の", 3_1_6_1_7),
							    ("武田信玄は、このような「武田家の家", 3_1_3_8_1),
							]
							for input, output in target:
												A     =   tokenizer.encode(__SCREAMING_SNAKE_CASE	,	return_tensors="pt")
												A     =   model(__SCREAMING_SNAKE_CASE)
												A     =   outputs.logits.detach().cpu().numpy()[0]
												A     =   np.argmax(output_logits[-1])
												self.assertEqual(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)


		@slow
		def    SCREAMING_SNAKE_CASE__  (self					:						Optional[int]):
							A     =   GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese")
							A     =   GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
							model.to(__SCREAMING_SNAKE_CASE)

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

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

							# 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=__SCREAMING_SNAKE_CASE	,	spout=spouts[0]	,	max_new_tokens=2_0	,	generation_config=__SCREAMING_SNAKE_CASE	,	)

							A     =   model.generate(
							    input_ids=__SCREAMING_SNAKE_CASE	,	spout=spouts[1]	,	max_new_tokens=2_0	,	generation_config=__SCREAMING_SNAKE_CASE	,	)

							A     =   model.generate(
							    input_ids=__SCREAMING_SNAKE_CASE	,	spout=__SCREAMING_SNAKE_CASE	,	max_new_tokens=2_0	,	generation_config=__SCREAMING_SNAKE_CASE	,	)

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

							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(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)
							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	[outa_sentence, outa_sentence])


		@slow
		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							A     =   GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese")
							A     =   GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese")
							model.to(__SCREAMING_SNAKE_CASE)

							# 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(__SCREAMING_SNAKE_CASE	,	prefix_text=__SCREAMING_SNAKE_CASE	,	return_tensors="pt")
							A     =   tokenizer(__SCREAMING_SNAKE_CASE	,	prefix_text=__SCREAMING_SNAKE_CASE	,	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(__SCREAMING_SNAKE_CASE)	,	token_type_ids=input_tok_a.token_type_ids.to(__SCREAMING_SNAKE_CASE)	,	max_new_tokens=2_0	,	generation_config=__SCREAMING_SNAKE_CASE	,	)

							A     =   model.generate(
							    input_ids=input_tok_a.input_ids.to(__SCREAMING_SNAKE_CASE)	,	token_type_ids=input_tok_a.token_type_ids.to(__SCREAMING_SNAKE_CASE)	,	max_new_tokens=2_0	,	generation_config=__SCREAMING_SNAKE_CASE	,	)

							A     =   model.generate(
							    input_ids=input_tok_a.input_ids.to(__SCREAMING_SNAKE_CASE)	,	token_type_ids=input_tok_a.token_type_ids.to(__SCREAMING_SNAKE_CASE)	,	attention_mask=input_tok_a.attention_mask.to(__SCREAMING_SNAKE_CASE)	,	max_new_tokens=2_0	,	generation_config=__SCREAMING_SNAKE_CASE	,	)

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

							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(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE)
							self.assertListEqual(__SCREAMING_SNAKE_CASE	,	[outa_sentence, outa_sentence])










def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						float  ,					_SCREAMING_SNAKE_CASE :						float					)->float:
      _lowerCAmelCase			      =	float()
      try:
            _lowerCAmelCase			      =	mass * acceleration
      except Exception:
            return -0.0
      return force


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()

    # demo
    UpperCAmelCase_          =				12.5
    UpperCAmelCase_          =				1_0
    UpperCAmelCase_          =				newtons_second_law_of_motion(mass, acceleration)
    print("The force is ", force, "N")