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   lowerCAmelCase    :



     def __init__(       self					,   lowerCAmelCase__					,   lowerCAmelCase__=36000					,   lowerCAmelCase__=13					,   lowerCAmelCase__=7					,   lowerCAmelCase__=True					,   lowerCAmelCase__=32					,   lowerCAmelCase__=42					,   lowerCAmelCase__=5					,   lowerCAmelCase__=2					,   lowerCAmelCase__=4					,   lowerCAmelCase__=2					,   lowerCAmelCase__=32					,   lowerCAmelCase__=80					,   lowerCAmelCase__=33					,   lowerCAmelCase__=0.0					,   lowerCAmelCase__=1E-6					,   lowerCAmelCase__=100					,   lowerCAmelCase__=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       a__	(       self							):
            return GPTSanJapaneseConfig.from_pretrained('Tanrei/GPTSAN-japanese'							)



     def       a__	(       self							):
            _A=						ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size							)

            _A=						self.get_config()

            return (config, input_ids)



     def       a__	(       self							):
            _A=						ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size							)

            _A=						self.get_config()

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



     def       a__	(       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       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__					,   ):
            _A=						GPTSanJapaneseForConditionalGeneration(config=lowerCAmelCase__							)
            model.to(lowerCAmelCase__							)
            model.eval()
            _A=						model(
                input_ids=lowerCAmelCase__					,   )
            self.parent.assertIsNotNone(lowerCAmelCase__							)





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



     def       a__	(       self					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__							):
            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       a__	(       self							):
            _A=						GPTSanJapaneseTester(self							)
            _A=						ConfigTester(self					,   config_class=lowerCAmelCase__					,   d_model=37							)



     def       a__	(       self							):
            GPTSanJapaneseConfig()



     def       a__	(       self							):
            _A=						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__							)



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





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





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



     def       a__	(       self							):
            _A=						GPTSanJapaneseTester(self							)
            _A=						ConfigTester(self					,   config_class=lowerCAmelCase__					,   d_model=37							)



     def       a__	(       self							):
            GPTSanJapaneseConfig()



     def       a__	(       self							):
            _A=						self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__							)



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



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



     @slow
     def       a__	(       self							):
            _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(lowerCAmelCase__							)
            _A=						outputs.logits.detach().cpu().numpy()
            # Output of original model created with mesh-tensoflow
            _A=						[
                # 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
            ]
            _A=						np.array(lowerCAmelCase__							).flatten()
            _A=						output_logits[0, :, :20].flatten()

            def check(lowerCAmelCase__					,   lowerCAmelCase__					,   lowerCAmelCase__=5E-4							):
                   return abs(a - b							) < epsilon * max(abs(lowerCAmelCase__							)					,   abs(lowerCAmelCase__							)							)

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



     @slow
     def       a__	(       self							):
            _A=						GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'							)
            _A=						GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'							)
            model.to(lowerCAmelCase__							)

            # 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(lowerCAmelCase__					,   return_tensors='pt'					,   padding=lowerCAmelCase__							)
            _A=						inputs['input_ids'].to(lowerCAmelCase__							)

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

            _A=						model.generate(
                input_ids=lowerCAmelCase__					,   attention_mask=inputs['attention_mask'].to(lowerCAmelCase__							)					,   max_new_tokens=3					,   generation_config=lowerCAmelCase__					,   )

            _A=						tokenizer(sentences[0]					,   return_tensors='pt'							).input_ids.to(lowerCAmelCase__							)
            _A=						model.generate(
                input_ids=lowerCAmelCase__					,   max_new_tokens=3					,   generation_config=lowerCAmelCase__							)

            _A=						tokenizer(sentences[1]					,   return_tensors='pt'							).input_ids.to(lowerCAmelCase__							)
            _A=						model.generate(input_ids=lowerCAmelCase__					,   max_new_tokens=3					,   generation_config=lowerCAmelCase__							)

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

            _A=						tokenizer.batch_decode(lowerCAmelCase__					,   skip_special_tokens=lowerCAmelCase__							)
            _A=						tokenizer.decode(output_non_padded[0]					,   skip_special_tokens=lowerCAmelCase__							)
            _A=						tokenizer.decode(output_padded[0]					,   skip_special_tokens=lowerCAmelCase__							)

            _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(lowerCAmelCase__					,   lowerCAmelCase__							)
            self.assertListEqual(lowerCAmelCase__					,   [non_padded_sentence, padded_sentence]							)



     @tooslow
     def       a__	(       self							):
            _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', 35675),
                ('武田信玄は、', 45),
                ('武田信玄は、この', 29),
                ('武田信玄は、このよう', 30642),
                ('武田信玄は、このような', 35680),
                ('武田信玄は、このような「', 8640),
                ('武田信玄は、このような「武田', 31617),
                ('武田信玄は、このような「武田家', 30646),
                ('武田信玄は、このような「武田家の', 31617),
                ('武田信玄は、このような「武田家の家', 31381),
            ]
            for input, output in target:
                   _A=						tokenizer.encode(lowerCAmelCase__					,   return_tensors='pt'							)
                   _A=						model(lowerCAmelCase__							)
                   _A=						outputs.logits.detach().cpu().numpy()[0]
                   _A=						np.argmax(output_logits[-1]							)
                   self.assertEqual(lowerCAmelCase__					,   lowerCAmelCase__							)



     @slow
     def       a__	(       self							):
            _A=						GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'							)
            _A=						GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'							)
            model.to(lowerCAmelCase__							)

            # set deterministically
            _A=						GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese'							)
            _A=						1

            _A=						'武田信玄は、'
            _A=						tokenizer(lowerCAmelCase__					,   return_tensors='pt'							).input_ids.to(lowerCAmelCase__							)
            _A=						tokenizer([input_text, input_text]					,   return_tensors='pt'							).input_ids.to(lowerCAmelCase__							)

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

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

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

            _A=						model.generate(
                input_ids=lowerCAmelCase__					,   spout=lowerCAmelCase__					,   max_new_tokens=20					,   generation_config=lowerCAmelCase__					,   )

            _A=						tokenizer.decode(outputa[0]							)
            _A=						tokenizer.decode(outputa[0]							)
            _A=						tokenizer.batch_decode(lowerCAmelCase__							)

            _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(lowerCAmelCase__					,   lowerCAmelCase__							)
            self.assertListEqual(lowerCAmelCase__					,   [outa_sentence, outa_sentence]							)





     @slow
     def       a__	(       self							):
            _A=						GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'							)
            _A=						GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'							)
            model.to(lowerCAmelCase__							)

            # 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(lowerCAmelCase__					,   prefix_text=lowerCAmelCase__					,   return_tensors='pt'							)
            _A=						tokenizer(lowerCAmelCase__					,   prefix_text=lowerCAmelCase__					,   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(lowerCAmelCase__							)					,   token_type_ids=input_tok_a.token_type_ids.to(lowerCAmelCase__							)					,   max_new_tokens=20					,   generation_config=lowerCAmelCase__					,   )

            _A=						model.generate(
                input_ids=input_tok_a.input_ids.to(lowerCAmelCase__							)					,   token_type_ids=input_tok_a.token_type_ids.to(lowerCAmelCase__							)					,   max_new_tokens=20					,   generation_config=lowerCAmelCase__					,   )

            _A=						model.generate(
                input_ids=input_tok_a.input_ids.to(lowerCAmelCase__							)					,   token_type_ids=input_tok_a.token_type_ids.to(lowerCAmelCase__							)					,   attention_mask=input_tok_a.attention_mask.to(lowerCAmelCase__							)					,   max_new_tokens=20					,   generation_config=lowerCAmelCase__					,   )

            _A=						tokenizer.decode(outputa[0]							)
            _A=						tokenizer.decode(outputa[0]							)
            _A=						tokenizer.batch_decode(lowerCAmelCase__							)

            _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(lowerCAmelCase__					,   lowerCAmelCase__							)
            self.assertListEqual(lowerCAmelCase__					,   [outa_sentence, outa_sentence]							)
from __future__ import annotations

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    set[int]:




       '''simple docstring'''






       _A=						{1}
       _A=						[1]
       for _ in range(2							,       depth + 1 ):
              _A=						[*previous_coefficients, 0]
              _A=						[0, *previous_coefficients]
              _A=						[]
              for x, y in zip(lowerCAmelCase_							,       lowerCAmelCase_ ):
                     coefficients.add(x + y )
                     previous_coefficients.append(x + y )
       return coefficients

def 				UpperCamelCase					(     lowerCAmelCase_ )		->    set[int]:




       '''simple docstring'''






       _A=						set()
       for number in unique_coefficients:
              _A=						2
              _A=						number
              while divisor**2 <= copy_number:
                     _A=						0
                     while copy_number % divisor == 0:
                            copy_number //= divisor
                            multiplicity += 1
                     if multiplicity >= 2:
                            non_squarefrees.add(lowerCAmelCase_ )
                            break
                     divisor += 1

       return unique_coefficients.difference(lowerCAmelCase_ )

def 				UpperCamelCase					(     lowerCAmelCase_ = 51 )		->    int:




       '''simple docstring'''






       _A=						get_pascal_triangle_unique_coefficients(lowerCAmelCase_ )
       _A=						get_squarefrees(lowerCAmelCase_ )
       return sum(lowerCAmelCase_ )


if __name__ == "__main__":
      print(F"{solution() = }")