


'''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    lowercase__  :





							'''simple docstring'''


							def __init__(   self   , __snake_case   , __snake_case=3_6000   , __snake_case=13   , __snake_case=7   , __snake_case=True   , __snake_case=32   , __snake_case=42   , __snake_case=5   , __snake_case=2   , __snake_case=4   , __snake_case=2   , __snake_case=32   , __snake_case=80   , __snake_case=33   , __snake_case=0.0   , __snake_case=1e-6   , __snake_case=100   , __snake_case=0.0   , ):
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				vocab_size
											_SCREAMING_SNAKE_CASE			:	List[str]							=				parent
											_SCREAMING_SNAKE_CASE			:	Tuple							=				batch_size
											_SCREAMING_SNAKE_CASE			:	List[str]							=				num_contexts
											# For common tests
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.num_contexts
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				is_training
											_SCREAMING_SNAKE_CASE			:	Any							=				hidden_size
											_SCREAMING_SNAKE_CASE			:	Dict							=				num_ext_layers
											_SCREAMING_SNAKE_CASE			:	str							=				ext_size
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				num_hidden_layers
											_SCREAMING_SNAKE_CASE			:	List[str]							=				num_attention_heads
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				num_experts
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				d_ff
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				d_ext
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				d_spout
											_SCREAMING_SNAKE_CASE			:	int							=				dropout_rate
											_SCREAMING_SNAKE_CASE			:	Any							=				layer_norm_epsilon
											_SCREAMING_SNAKE_CASE			:	str							=				expert_capacity
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				router_jitter_noise






							def UpperCAmelCase_     (   self  ):
											return GPTSanJapaneseConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Tuple							=				ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size  )

											_SCREAMING_SNAKE_CASE			:	Tuple							=				self.get_config()

											return (config, input_ids)






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				ids_tensor([self.batch_size, self.seq_length]   , self.vocab_size  )

											_SCREAMING_SNAKE_CASE			:	Tuple							=				self.get_config()

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






							def UpperCAmelCase_     (   self  ):
											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 UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , ):
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				GPTSanJapaneseForConditionalGeneration(config=lowercase_  )
											model.to(lowercase_  )
											model.eval()
											_SCREAMING_SNAKE_CASE			:	Any							=				model(
											    input_ids=lowercase_   , )
											self.parent.assertIsNotNone(lowercase_  )





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





							'''simple docstring'''


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






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case   , __snake_case   , __snake_case   , __snake_case  ):






											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 UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				GPTSanJapaneseTester(self  )
											_SCREAMING_SNAKE_CASE			:	Any							=				ConfigTester(self   , config_class=lowercase_   , d_model=37  )






							def UpperCAmelCase_     (   self  ):
											GPTSanJapaneseConfig()






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	str							=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowercase_  )






							@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 UpperCAmelCase_     (   self  ):
											super().test_model_parallelism()






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





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





							'''simple docstring'''


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






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Tuple							=				GPTSanJapaneseTester(self  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				ConfigTester(self   , config_class=lowercase_   , d_model=37  )






							def UpperCAmelCase_     (   self  ):
											GPTSanJapaneseConfig()






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*lowercase_  )






							@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 UpperCAmelCase_     (   self  ):
											super().test_model_parallelism()






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






							@slow
							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
											_SCREAMING_SNAKE_CASE			:	Tuple							=				tokenizer.encode("""武田信玄は"""   , return_tensors="""pt"""  )
											_SCREAMING_SNAKE_CASE			:	Tuple							=				model(lowercase_  )
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				outputs.logits.detach().cpu().numpy()
											# Output of original model created with mesh-tensoflow
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				[
											    # fmt: off
											    [-12.037839889526367, -12.433061599731445, -14.333840370178223, -12.450345993041992, -11.1661376953125,
											    -11.930137634277344, -10.659740447998047, -12.909574508666992, -13.241043090820312, -13.398579597473145,
											    -11.107524871826172, -12.3685941696167, -22.97943115234375, -10.481067657470703, -12.484030723571777,
											    -12.807360649108887, -14.769700050354004, -12.233579635620117, -13.428145408630371, -22.624177932739258],
											    [-7.511149883270264, -8.281851768493652, -7.943127155303955, -7.55021333694458, -6.49869966506958,
											    -7.586796283721924, -6.978085994720459, -7.839145183563232, -8.21964168548584, -8.695091247558594,
											    -6.706910610198975, -6.6585798263549805, -19.565698623657227, -5.353842735290527, -8.350686073303223,
											    -8.039388656616211, -10.856569290161133, -7.75154447555542, -8.819022178649902, -19.51532745361328],
											    [-9.73066234588623, -10.223922729492188, -9.932981491088867, -11.857836723327637, -7.662626266479492,
											    -11.13529109954834, -7.765097618103027, -11.472923278808594, -9.543149948120117, -11.905633926391602,
											    -9.366164207458496, -11.5734281539917, -23.699003219604492, -9.429590225219727, -10.42839241027832,
											    -10.585240364074707, -10.94771957397461, -11.095416069030762, -10.390240669250488, -23.769372940063477],
											    [-9.728265762329102, -9.859712600708008, -10.09729290008545, -9.678522109985352, -6.879519939422607,
											    -9.68487548828125, -4.2803425788879395, -10.018914222717285, -9.308445930480957, -10.63394546508789,
											    -8.083646774291992, -9.06301498413086, -21.904266357421875, -8.90160846710205, -8.841876029968262,
											    -11.856719970703125, -12.079398155212402, -11.233753204345703, -10.177338600158691, -21.87256622314453],
											    [-9.669764518737793, -9.614198684692383, -9.814510345458984, -9.996501922607422, -11.375690460205078,
											    -10.113405227661133, -10.546867370605469, -10.04369068145752, -10.907809257507324, -10.504216194152832,
											    -11.129199028015137, -10.151124000549316, -21.96586799621582, -9.086349487304688, -11.730339050292969,
											    -10.460667610168457, -10.298049926757812, -10.784148216247559, -10.840693473815918, -22.03152847290039],
											    # fmt: on
											]
											_SCREAMING_SNAKE_CASE			:	Dict							=				np.array(lowercase_  ).flatten()
											_SCREAMING_SNAKE_CASE			:	Any							=				output_logits[0, :, :20].flatten()

											def check(__snake_case   , __snake_case   , __snake_case=5e-4  ):
															return abs(a - b  ) < epsilon * max(abs(lowercase_  )   , abs(lowercase_  )  )

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






							@slow
							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Tuple							=				GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
											model.to(lowercase_  )

											# set deterministically
											_SCREAMING_SNAKE_CASE			:	int							=				GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
											_SCREAMING_SNAKE_CASE			:	str							=				1

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

											_SCREAMING_SNAKE_CASE			:	str							=				"""left"""
											_SCREAMING_SNAKE_CASE			:	Dict							=				tokenizer(lowercase_   , return_tensors="""pt"""   , padding=lowercase_  )
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				inputs["""input_ids"""].to(lowercase_  )

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

											_SCREAMING_SNAKE_CASE			:	int							=				model.generate(
											    input_ids=lowercase_   , attention_mask=inputs["""attention_mask"""].to(lowercase_  )   , max_new_tokens=3   , generation_config=lowercase_   , )

											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				tokenizer(sentences[0]   , return_tensors="""pt"""  ).input_ids.to(lowercase_  )
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				model.generate(
											    input_ids=lowercase_   , max_new_tokens=3   , generation_config=lowercase_  )

											_SCREAMING_SNAKE_CASE			:	int							=				tokenizer(sentences[1]   , return_tensors="""pt"""  ).input_ids.to(lowercase_  )
											_SCREAMING_SNAKE_CASE			:	List[str]							=				model.generate(input_ids=lowercase_   , max_new_tokens=3   , generation_config=lowercase_  )

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

											_SCREAMING_SNAKE_CASE			:	str							=				tokenizer.batch_decode(lowercase_   , skip_special_tokens=lowercase_  )
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				tokenizer.decode(output_non_padded[0]   , skip_special_tokens=lowercase_  )
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				tokenizer.decode(output_padded[0]   , skip_special_tokens=lowercase_  )

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				[
											    """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(lowercase_   , lowercase_  )
											self.assertListEqual(lowercase_   , [non_padded_sentence, padded_sentence]  )






							@tooslow
							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
											# Output of original model created with mesh-tensoflow
											_SCREAMING_SNAKE_CASE			:	str							=				[
											    ("""武田信玄は""", 3_5675),
											    ("""武田信玄は、""", 45),
											    ("""武田信玄は、この""", 29),
											    ("""武田信玄は、このよう""", 3_0642),
											    ("""武田信玄は、このような""", 3_5680),
											    ("""武田信玄は、このような「""", 8640),
											    ("""武田信玄は、このような「武田""", 3_1617),
											    ("""武田信玄は、このような「武田家""", 3_0646),
											    ("""武田信玄は、このような「武田家の""", 3_1617),
											    ("""武田信玄は、このような「武田家の家""", 3_1381),
											]
											for input, output in target:
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				tokenizer.encode(lowercase_   , return_tensors="""pt"""  )
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				model(lowercase_  )
															_SCREAMING_SNAKE_CASE			:	str							=				outputs.logits.detach().cpu().numpy()[0]
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				np.argmax(output_logits[-1]  )
															self.assertEqual(lowercase_   , lowercase_  )






							@slow
							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
											_SCREAMING_SNAKE_CASE			:	int							=				GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
											model.to(lowercase_  )

											# set deterministically
											_SCREAMING_SNAKE_CASE			:	List[str]							=				GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
											_SCREAMING_SNAKE_CASE			:	str							=				1

											_SCREAMING_SNAKE_CASE			:	Any							=				"""武田信玄は、"""
											_SCREAMING_SNAKE_CASE			:	List[str]							=				tokenizer(lowercase_   , return_tensors="""pt"""  ).input_ids.to(lowercase_  )
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				tokenizer([input_text, input_text]   , return_tensors="""pt"""  ).input_ids.to(lowercase_  )

											# spout from uniform and one-hot
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				[
											    # fmt: off
											    [0.87882208, 0.38426396, 0.33220248, 0.43890406, 0.16562252,
											    0.04803985, 0.211572  , 0.23188473, 0.37153068, 0.7836377 ,
											    0.02160172, 0.38761719, 0.75290772, 0.90198857, 0.34365777,
											    0.64168169, 0.44318471, 0.14575746, 0.92562881, 0.40812148,
											    0.29019122, 0.88861599, 0.65524846, 0.43563456, 0.38177187,
											    0.70832965, 0.81527892, 0.68832812, 0.38833192, 0.4561522 ,
											    0.14828817, 0.47248213, 0.54357335, 0.82009566, 0.1338884 ,
											    0.02755417, 0.19764677, 0.2422084 , 0.04757674, 0.65409606,
											    0.0824589 , 0.03304383, 0.94387689, 0.98764509, 0.82433901,
											    0.27646741, 0.64907493, 0.76009406, 0.30087915, 0.17904689,
											    0.41601714, 0.67046398, 0.10422822, 0.08447374, 0.07354344,
											    0.61423565, 0.70284866, 0.7532333 , 0.1972038 , 0.29575659,
											    0.90583886, 0.29265307, 0.50000175, 0.70407655, 0.889363  ,
											    0.81904418, 0.66829128, 0.64468815, 0.56563723, 0.85601875,
											    0.94924672, 0.00166762, 0.25220643, 0.74540219, 0.67993247,
											    0.1549675 , 0.39385352, 0.92153607, 0.63745931, 0.27759043,
											    0.84702295, 0.65904271, 0.58676614, 0.8666936 , 0.39607438,
											    0.79954983, 0.42220697, 0.39650381, 0.7849864 , 0.56150201,
											    0.15678925, 0.14746032, 0.34542114, 0.47026783, 0.11956489,
											    0.25421435, 0.33788901, 0.68934842, 0.36424685, 0.71737898,
											    0.38983449, 0.94393779, 0.39575588, 0.36616553, 0.87104665,
											    0.64630203, 0.22516905, 0.88270804, 0.15031338, 0.75144345,
											    0.46459025, 0.85396454, 0.86355643, 0.65139851, 0.70266061,
											    0.30241389, 0.81056497, 0.88865969, 0.38773807, 0.70635849,
											    0.90718459, 0.43245789, 0.28000654, 0.45935562, 0.08773519,
											    0.9552151 , 0.93901511, 0.22489288], # 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
											]

											_SCREAMING_SNAKE_CASE			:	Tuple							=				model.generate(
											    input_ids=lowercase_   , spout=spouts[0]   , max_new_tokens=20   , generation_config=lowercase_   , )

											_SCREAMING_SNAKE_CASE			:	str							=				model.generate(
											    input_ids=lowercase_   , spout=spouts[1]   , max_new_tokens=20   , generation_config=lowercase_   , )

											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				model.generate(
											    input_ids=lowercase_   , spout=lowercase_   , max_new_tokens=20   , generation_config=lowercase_   , )

											_SCREAMING_SNAKE_CASE			:	List[str]							=				tokenizer.decode(outputa[0]  )
											_SCREAMING_SNAKE_CASE			:	Tuple							=				tokenizer.decode(outputa[0]  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				tokenizer.batch_decode(lowercase_  )

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				[
											    """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(lowercase_   , lowercase_  )
											self.assertListEqual(lowercase_   , [outa_sentence, outa_sentence]  )






							@slow
							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Any							=				GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
											model.to(lowercase_  )

											# set deterministically
											_SCREAMING_SNAKE_CASE			:	Tuple							=				GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
											_SCREAMING_SNAKE_CASE			:	List[str]							=				1

											_SCREAMING_SNAKE_CASE			:	str							=				"""武田信玄"""
											_SCREAMING_SNAKE_CASE			:	int							=				"""織田信長"""
											_SCREAMING_SNAKE_CASE			:	str							=				"""は、"""
											_SCREAMING_SNAKE_CASE			:	Dict							=				"""が、"""
											_SCREAMING_SNAKE_CASE			:	List[str]							=				tokenizer(lowercase_   , prefix_text=lowercase_   , return_tensors="""pt"""  )
											_SCREAMING_SNAKE_CASE			:	int							=				tokenizer(lowercase_   , prefix_text=lowercase_   , return_tensors="""pt"""  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]   , return_tensors="""pt"""  )

											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				model.generate(
											    input_ids=input_tok_a.input_ids.to(lowercase_  )   , token_type_ids=input_tok_a.token_type_ids.to(lowercase_  )   , max_new_tokens=20   , generation_config=lowercase_   , )

											_SCREAMING_SNAKE_CASE			:	Tuple							=				model.generate(
											    input_ids=input_tok_a.input_ids.to(lowercase_  )   , token_type_ids=input_tok_a.token_type_ids.to(lowercase_  )   , max_new_tokens=20   , generation_config=lowercase_   , )

											_SCREAMING_SNAKE_CASE			:	Any							=				model.generate(
											    input_ids=input_tok_a.input_ids.to(lowercase_  )   , token_type_ids=input_tok_a.token_type_ids.to(lowercase_  )   , attention_mask=input_tok_a.attention_mask.to(lowercase_  )   , max_new_tokens=20   , generation_config=lowercase_   , )

											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				tokenizer.decode(outputa[0]  )
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				tokenizer.decode(outputa[0]  )
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				tokenizer.batch_decode(lowercase_  )

											_SCREAMING_SNAKE_CASE			:	Any							=				[
											    """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(lowercase_   , lowercase_  )
											self.assertListEqual(lowercase_   , [outa_sentence, outa_sentence]  )








'''simple docstring'''


from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_torch_available():
				from ..models.auto.modeling_auto import (
				    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
				    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
				    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
				    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
				)


UpperCAmelCase_		:       Optional[int]							       =		logging.get_logger(__name__)


UpperCAmelCase_		:       List[str]							       =		Dict[str, Any]
UpperCAmelCase_		:       List[str]							       =		List[Prediction]





@add_end_docstrings(_snake_case )
class    lowercase__  (						_snake_case ):





							'''simple docstring'''


							def __init__(   self   , *__snake_case   , **__snake_case  ):
											super().__init__(*__snake_case   , **__snake_case  )

											if self.framework == "tf":
															raise ValueError(f"""The {self.__class__} is only available in PyTorch."""  )

											requires_backends(self   , """vision"""  )
											self.check_model_type(
											    dict(
											        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
											        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
											        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
											        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()  )  )






							def UpperCAmelCase_     (   self   , **__snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				{}
											_SCREAMING_SNAKE_CASE			:	Any							=				{}
											if "subtask" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Any							=				kwargs["""subtask"""]
															_SCREAMING_SNAKE_CASE			:	List[str]							=				kwargs["""subtask"""]
											if "threshold" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				kwargs["""threshold"""]
											if "mask_threshold" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Dict							=				kwargs["""mask_threshold"""]
											if "overlap_mask_area_threshold" in kwargs:
															_SCREAMING_SNAKE_CASE			:	Tuple							=				kwargs["""overlap_mask_area_threshold"""]

											return preprocess_kwargs, {}, postprocess_kwargs






							def __call__(   self   , __snake_case   , **__snake_case  ):
											return super().__call__(__snake_case   , **__snake_case  )






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case=None  ):
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				load_image(__snake_case  )
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				[(image.height, image.width)]
											if self.model.config.__class__.__name__ == "OneFormerConfig":
															if subtask is None:
																			_SCREAMING_SNAKE_CASE			:	int							=				{}
															else:
																			_SCREAMING_SNAKE_CASE			:	str							=				{"""task_inputs""": [subtask]}
															_SCREAMING_SNAKE_CASE			:	Any							=				self.image_processor(images=[image]   , return_tensors="""pt"""   , **__snake_case  )
															_SCREAMING_SNAKE_CASE			:	List[Any]							=				self.tokenizer(
															    inputs["""task_inputs"""]   , padding="""max_length"""   , max_length=self.model.config.task_seq_len   , return_tensors=self.framework   , )["""input_ids"""]
											else:
															_SCREAMING_SNAKE_CASE			:	int							=				self.image_processor(images=[image]   , return_tensors="""pt"""  )
											_SCREAMING_SNAKE_CASE			:	Dict							=				target_size
											return inputs






							def UpperCAmelCase_     (   self   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	Dict							=				model_inputs.pop("""target_size"""  )
											_SCREAMING_SNAKE_CASE			:	int							=				self.model(**__snake_case  )
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				target_size
											return model_outputs






							def UpperCAmelCase_     (   self   , __snake_case   , __snake_case=None   , __snake_case=0.9   , __snake_case=0.5   , __snake_case=0.5  ):
											_SCREAMING_SNAKE_CASE			:	str							=				None
											if subtask in {"panoptic", None} and hasattr(self.image_processor   , """post_process_panoptic_segmentation"""  ):
															_SCREAMING_SNAKE_CASE			:	int							=				self.image_processor.post_process_panoptic_segmentation
											elif subtask in {"instance", None} and hasattr(self.image_processor   , """post_process_instance_segmentation"""  ):
															_SCREAMING_SNAKE_CASE			:	str							=				self.image_processor.post_process_instance_segmentation

											if fn is not None:
															_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				fn(
															    __snake_case   , threshold=__snake_case   , mask_threshold=__snake_case   , overlap_mask_area_threshold=__snake_case   , target_sizes=model_outputs["""target_size"""]   , )[0]

															_SCREAMING_SNAKE_CASE			:	Dict							=				[]
															_SCREAMING_SNAKE_CASE			:	int							=				outputs["""segmentation"""]

															for segment in outputs["segments_info"]:
																			_SCREAMING_SNAKE_CASE			:	str							=				(segmentation == segment["""id"""]) * 255
																			_SCREAMING_SNAKE_CASE			:	int							=				Image.fromarray(mask.numpy().astype(np.uinta  )   , mode="""L"""  )
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				self.model.config.idalabel[segment["""label_id"""]]
																			_SCREAMING_SNAKE_CASE			:	List[Any]							=				segment["""score"""]
																			annotation.append({"""score""": score, """label""": label, """mask""": mask}  )

											elif subtask in {"semantic", None} and hasattr(self.image_processor   , """post_process_semantic_segmentation"""  ):
															_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.image_processor.post_process_semantic_segmentation(
															    __snake_case   , target_sizes=model_outputs["""target_size"""]  )[0]

															_SCREAMING_SNAKE_CASE			:	str							=				[]
															_SCREAMING_SNAKE_CASE			:	List[str]							=				outputs.numpy()
															_SCREAMING_SNAKE_CASE			:	Optional[int]							=				np.unique(__snake_case  )

															for label in labels:
																			_SCREAMING_SNAKE_CASE			:	Any							=				(segmentation == label) * 255
																			_SCREAMING_SNAKE_CASE			:	Tuple							=				Image.fromarray(mask.astype(np.uinta  )   , mode="""L"""  )
																			_SCREAMING_SNAKE_CASE			:	Tuple							=				self.model.config.idalabel[label]
																			annotation.append({"""score""": None, """label""": label, """mask""": mask}  )
											else:
															raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model  )}"""  )
											return annotation




