




'''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 				_snake_case	:




			def __init__(      self       ,							a__       ,							a__=36_000       ,							a__=13       ,							a__=7       ,							a__=True       ,							a__=32       ,							a__=42       ,							a__=5       ,							a__=2       ,							a__=4       ,							a__=2       ,							a__=32       ,							a__=80       ,							a__=33       ,							a__=0.0       ,							a__=1e-6       ,							a__=100       ,							a__=0.0       ,							)       ->       str:

										'''simple docstring'''




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




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

										'''simple docstring'''




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




			def      lowerCAmelCase__       (      self							)       ->       Any:

										'''simple docstring'''




										snake_case_			      =   ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size							)

										snake_case_			      =   self.get_config()

										return (config, input_ids)




			def      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size							)

										snake_case_			      =   self.get_config()

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




			def      lowerCAmelCase__       (      self							)       ->       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      lowerCAmelCase__       (      self       ,							a__       ,							a__       ,							)       ->       Tuple:

										'''simple docstring'''




										snake_case_			      =   GPTSanJapaneseForConditionalGeneration(config=a__							)
										model.to(a__							)
										model.eval()
										snake_case_			      =   model(
										    input_ids=a__       ,							)
										self.parent.assertIsNotNone(a__							)




@require_torch
class 				_snake_case	(			lowercase_		,		lowercase_		,		unittest.TestCase							):
			lowerCAmelCase_  :   List[str]		 =					(GPTSanJapaneseModel,) if is_torch_available() else ()
			lowerCAmelCase_  :   str		 =					(
			    {
			        "conversational": GPTSanJapaneseForConditionalGeneration,
			        "feature-extraction": GPTSanJapaneseForConditionalGeneration,
			        "summarization": GPTSanJapaneseForConditionalGeneration,
			        "text2text-generation": GPTSanJapaneseForConditionalGeneration,
			        "translation": GPTSanJapaneseForConditionalGeneration,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCAmelCase_  :   Dict		 =					False
			lowerCAmelCase_  :   Dict		 =					False
			lowerCAmelCase_  :   int		 =					False
			lowerCAmelCase_  :   Any		 =					False
			lowerCAmelCase_  :   Union[str, Any]		 =					False
			lowerCAmelCase_  :   List[Any]		 =					False
			lowerCAmelCase_  :   List[Any]		 =					False
			lowerCAmelCase_  :   List[Any]		 =					False
			# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
			lowerCAmelCase_  :   Dict		 =					[0.8, 0.9]




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

										'''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      lowerCAmelCase__       (      self							)       ->       Optional[Any]:

										'''simple docstring'''




										snake_case_			      =   GPTSanJapaneseTester(self							)
										snake_case_			      =   ConfigTester(self       ,							config_class=a__       ,							d_model=37							)




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

										'''simple docstring'''




										GPTSanJapaneseConfig()




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

										'''simple docstring'''




										snake_case_			      =   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      lowerCAmelCase__       (      self							)       ->       Tuple:

										'''simple docstring'''




										super().test_model_parallelism()




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

										'''simple docstring'''




										pass




@require_torch
class 				_snake_case	(			lowercase_		,		lowercase_		,		unittest.TestCase							):
			lowerCAmelCase_  :   Union[str, Any]		 =					(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
			lowerCAmelCase_  :   Dict		 =					False
			lowerCAmelCase_  :   Optional[Any]		 =					False
			lowerCAmelCase_  :   int		 =					False
			lowerCAmelCase_  :   Optional[int]		 =					False
			lowerCAmelCase_  :   Optional[Any]		 =					False
			lowerCAmelCase_  :   Tuple		 =					False
			# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
			lowerCAmelCase_  :   Tuple		 =					[0.8, 0.9]




			def      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   GPTSanJapaneseTester(self							)
										snake_case_			      =   ConfigTester(self       ,							config_class=a__       ,							d_model=37							)




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

										'''simple docstring'''




										GPTSanJapaneseConfig()




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

										'''simple docstring'''




										snake_case_			      =   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      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




										super().test_model_parallelism()




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

										'''simple docstring'''




										pass




			@slow
			def      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




										snake_case_			      =   GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"							)
										snake_case_			      =   GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"							)
										snake_case_			      =   tokenizer.encode("武田信玄は"       ,							return_tensors="pt"							)
										snake_case_			      =   model(a__							)
										snake_case_			      =   outputs.logits.detach().cpu().numpy()
										# Output of original model created with mesh-tensoflow
										snake_case_			      =   [
										    # 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
										]
										snake_case_			      =   np.array(a__							).flatten()
										snake_case_			      =   output_logits[0, :, :20].flatten()

										def check(a__       ,							a__       ,							a__=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      lowerCAmelCase__       (      self							)       ->       str:

										'''simple docstring'''




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

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

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

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

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

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

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

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

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

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

										snake_case_			      =   [
										    "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      lowerCAmelCase__       (      self							)       ->       List[str]:

										'''simple docstring'''




										snake_case_			      =   GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"							)
										snake_case_			      =   GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"							)
										# Output of original model created with mesh-tensoflow
										snake_case_			      =   [
										    ("武田信玄は", 35_675),
										    ("武田信玄は、", 45),
										    ("武田信玄は、この", 29),
										    ("武田信玄は、このよう", 30_642),
										    ("武田信玄は、このような", 35_680),
										    ("武田信玄は、このような「", 8_640),
										    ("武田信玄は、このような「武田", 31_617),
										    ("武田信玄は、このような「武田家", 30_646),
										    ("武田信玄は、このような「武田家の", 31_617),
										    ("武田信玄は、このような「武田家の家", 31_381),
										]
										for input, output in target:
																	snake_case_			      =   tokenizer.encode(a__       ,							return_tensors="pt"							)
																	snake_case_			      =   model(a__							)
																	snake_case_			      =   outputs.logits.detach().cpu().numpy()[0]
																	snake_case_			      =   np.argmax(output_logits[-1]							)
																	self.assertEqual(a__       ,							a__							)




			@slow
			def      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




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

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

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

										# spout from uniform and one-hot
										snake_case_			      =   [
										    # 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
										]

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

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

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

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

										snake_case_			      =   [
										    "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      lowerCAmelCase__       (      self							)       ->       int:

										'''simple docstring'''




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

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

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

										snake_case_			      =   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__       ,							)

										snake_case_			      =   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__       ,							)

										snake_case_			      =   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__       ,							)

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

										snake_case_			      =   [
										    "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]							)










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_SCREAMING_SNAKE_CASE							:     Dict   	=				{
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							:     Optional[Any]   	=				[
		    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MobileViTV2ForImageClassification",
		    "MobileViTV2ForSemanticSegmentation",
		    "MobileViTV2Model",
		    "MobileViTV2PreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_mobilevitva import (
		    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    MobileViTVaConfig,
		    MobileViTVaOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTVaForImageClassification,
				    MobileViTVaForSemanticSegmentation,
				    MobileViTVaModel,
				    MobileViTVaPreTrainedModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE							:     Any   	=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




