





"""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_			:

       def __init__(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE=3_6000				,   __SCREAMING_SNAKE_CASE=13				,   __SCREAMING_SNAKE_CASE=7				,   __SCREAMING_SNAKE_CASE=True				,   __SCREAMING_SNAKE_CASE=32				,   __SCREAMING_SNAKE_CASE=42				,   __SCREAMING_SNAKE_CASE=5				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=4				,   __SCREAMING_SNAKE_CASE=2				,   __SCREAMING_SNAKE_CASE=32				,   __SCREAMING_SNAKE_CASE=80				,   __SCREAMING_SNAKE_CASE=33				,   __SCREAMING_SNAKE_CASE=0.0				,   __SCREAMING_SNAKE_CASE=1e-6				,   __SCREAMING_SNAKE_CASE=100				,   __SCREAMING_SNAKE_CASE=0.0				,   ):






        '''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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''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 						_SCREAMING_SNAKE_CASE(     self       ):






        '''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				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        snake_case__      							=			GPTSanJapaneseForConditionalGeneration(config=__SCREAMING_SNAKE_CASE       )
        model.to(__SCREAMING_SNAKE_CASE       )
        model.eval()
        snake_case__      							=			model(
            input_ids=__SCREAMING_SNAKE_CASE				,   )
        self.parent.assertIsNotNone(__SCREAMING_SNAKE_CASE       )



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

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ):






        '''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       ):






        '''simple docstring'''

        snake_case__      							=			GPTSanJapaneseTester(self       )
        snake_case__      							=			ConfigTester(self				,   config_class=__SCREAMING_SNAKE_CASE				,   d_model=37       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        GPTSanJapaneseConfig()

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			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       ):






        '''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       ):






        '''simple docstring'''

        pass



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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			GPTSanJapaneseTester(self       )
        snake_case__      							=			ConfigTester(self				,   config_class=__SCREAMING_SNAKE_CASE				,   d_model=37       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        GPTSanJapaneseConfig()

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			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       ):






        '''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       ):






        '''simple docstring'''

        pass

       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''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(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			outputs.logits.detach().cpu().numpy()
        # Output of original model created with mesh-tensoflow
        snake_case__      							=			[
            # 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
        ]
        snake_case__      							=			np.array(__SCREAMING_SNAKE_CASE       ).flatten()
        snake_case__      							=			output_logits[0, :, :20].flatten()

        def check(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE=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       ):






        '''simple docstring'''

        snake_case__      							=			GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"       )
        snake_case__      							=			GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"       )
        model.to(__SCREAMING_SNAKE_CASE       )

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

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

        snake_case__      							=			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				,   )

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

        snake_case__      							=			tokenizer(sentences[1]				,   return_tensors="pt"       ).input_ids.to(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			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       )

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

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

       @tooslow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''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", 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:
         snake_case__      							=			tokenizer.encode(__SCREAMING_SNAKE_CASE				,   return_tensors="pt"       )
         snake_case__      							=			model(__SCREAMING_SNAKE_CASE       )
         snake_case__      							=			outputs.logits.detach().cpu().numpy()[0]
         snake_case__      							=			np.argmax(output_logits[-1]       )
         self.assertEqual(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"       )
        snake_case__      							=			GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"       )
        model.to(__SCREAMING_SNAKE_CASE       )

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

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

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

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

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

        snake_case__      							=			model.generate(
            input_ids=__SCREAMING_SNAKE_CASE				,   spout=__SCREAMING_SNAKE_CASE				,   max_new_tokens=20				,   generation_config=__SCREAMING_SNAKE_CASE				,   )

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

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

       @slow
       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"       )
        snake_case__      							=			GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"       )
        model.to(__SCREAMING_SNAKE_CASE       )

        # 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(__SCREAMING_SNAKE_CASE				,   prefix_text=__SCREAMING_SNAKE_CASE				,   return_tensors="pt"       )
        snake_case__      							=			tokenizer(__SCREAMING_SNAKE_CASE				,   prefix_text=__SCREAMING_SNAKE_CASE				,   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(__SCREAMING_SNAKE_CASE       )				,   token_type_ids=input_tok_a.token_type_ids.to(__SCREAMING_SNAKE_CASE       )				,   max_new_tokens=20				,   generation_config=__SCREAMING_SNAKE_CASE				,   )

        snake_case__      							=			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=20				,   generation_config=__SCREAMING_SNAKE_CASE				,   )

        snake_case__      							=			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=20				,   generation_config=__SCREAMING_SNAKE_CASE				,   )

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

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








"""simple docstring"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Optional[int]										=       (LMSDiscreteScheduler,)
       snake_case__			:							int										=       10

       def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			{
            "num_train_timesteps": 1100,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**__SCREAMING_SNAKE_CASE       )
        return config

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]				,   [0.0002, 0.002, 0.02]       ):
         self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE				,   beta_end=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for t in [0, 500, 800]:
         self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 1006.388       ) < 1e-2
        assert abs(result_mean.item() - 1.31       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config(prediction_type="v_prediction"       )
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 0.0017       ) < 1e-2
        assert abs(result_mean.item() - 2.2676e-06       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps				,   device=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        snake_case__      							=			sample.to(__SCREAMING_SNAKE_CASE       )

        for i, t in enumerate(scheduler.timesteps       ):
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 1006.388       ) < 1e-2
        assert abs(result_mean.item() - 1.31       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE				,   use_karras_sigmas=__SCREAMING_SNAKE_CASE       )

        scheduler.set_timesteps(self.num_inference_steps				,   device=__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter.to(__SCREAMING_SNAKE_CASE       ) * scheduler.init_noise_sigma
        snake_case__      							=			sample.to(__SCREAMING_SNAKE_CASE       )

        for t in scheduler.timesteps:
         snake_case__      							=			scheduler.scale_model_input(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			model(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

         snake_case__      							=			scheduler.step(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
         snake_case__      							=			output.prev_sample

        snake_case__      							=			torch.sum(torch.abs(__SCREAMING_SNAKE_CASE       )       )
        snake_case__      							=			torch.mean(torch.abs(__SCREAMING_SNAKE_CASE       )       )

        assert abs(result_sum.item() - 3812.9927       ) < 2e-2
        assert abs(result_mean.item() - 4.9648       ) < 1e-3

