

import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


lowerCamelCase									= logging.getLogger(__name__)




def       UpperCAmelCase__    (      _A				:			int		):





  '''simple docstring'''







  random.seed(_lowerCamelCase		)
  np.random.seed(_lowerCamelCase		)
  torch.manual_seed(_lowerCamelCase		)
  torch.cuda.manual_seed_all(_lowerCamelCase		)




def       UpperCAmelCase__    (      _A				:			Optional[Any]     ,				_A				:			Tuple     ,				_A				:			List[Any]		):





  '''simple docstring'''







  model.eval()
  a__  =next(model.parameters()		).device
  a__  =1
  a__  =torch.zeros((eval_batch_size, context_len)     ,				dtype=torch.long     ,				device=_lowerCamelCase		)
  a__  =DataLoader(_lowerCamelCase     ,				shuffle=_lowerCamelCase     ,				batch_size=_lowerCamelCase		)
  a__  =torch.zeros(1     ,				device=_lowerCamelCase		)
  a__  =0
  for batch in eval_dataloader:
    batch.to(_lowerCamelCase		)
    # pad
    context.zero_()
    for i in range(_lowerCamelCase		):
      a__  =batch[i]
    a__  =model(_lowerCamelCase     ,				labels=_lowerCamelCase		)
    eval_loss += outputs[0].sum().item()
    nb_eval_examples += batch.size(0		)
  a__  =eval_loss / nb_eval_examples
  a__  =torch.exp(_lowerCamelCase		)
  model.train()
  return perplexity




def       UpperCAmelCase__    (      _A				:			Optional[Any]="gpt2"		):





  '''simple docstring'''







  a__  =GPTaLMHeadModel.from_pretrained(_lowerCamelCase     ,				output_hidden_states=_lowerCamelCase		)
  torch.save(model.state_dict()     ,				model_name + '''local.pt'''		)
  return model




def       UpperCAmelCase__    (      _A				:			Dict     ,				_A				:			Union[str, Any]     ,				_A				:			Tuple		):





  '''simple docstring'''







  a__  =copy.deepcopy(_lowerCamelCase		)
  model.to(_lowerCamelCase		)

  a__  =["""bias""", """LayerNorm.weight"""]
  a__  =[
      {
          """params""": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay		)],
          """weight_decay""": 0.0,
      },
      {"""params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay		)], """weight_decay""": 0.0},
  ]
  a__  =AdamW(_lowerCamelCase     ,				lr=5E-5     ,				eps=1E-8		)
  a__  =get_linear_schedule_with_warmup(_lowerCamelCase     ,				0     ,				_lowerCamelCase		)
  torch.cuda.empty_cache()
  return model, lm_optimizer, lm_scheduler




def       UpperCAmelCase__    (      _A				:			List[Any]     ,				_A				:			Optional[int]     ,				_A				:			int     ,				_A				:			Dict		):





  '''simple docstring'''







  a__  =np.array(_lowerCamelCase		).mean()
  a__  =np.array(_lowerCamelCase		).std()
  a__  =(real_perps - avg) / std
  a__  =list(zip(_lowerCamelCase     ,				_lowerCamelCase     ,				_lowerCamelCase		)		)
  joblib.dump(_lowerCamelCase     ,				_lowerCamelCase		)




def       UpperCAmelCase__    (      _A				:			Dict     ,				_A				:			Optional[int]     ,				_A				:			Dict     ,				_A				:			str     ,				_A				:			str     ,				_A				:			Any     ,				_A				:			Tuple     ,				_A				:			Optional[Any]="dev.jbl"     ,				_A				:			Optional[Any]=recopy_gpta     ,				):





  '''simple docstring'''







  a__  =[]
  a__  =[]
  a__  =[]

  # Initialize the transformer model
  a__  =copy.deepcopy(_lowerCamelCase		)
  orig_model.to(device='''cpu'''		)
  torch.cuda.empty_cache()

  # Compute perplexity of initial transformer model for comparison
  model.train()
  a__  =recopy_model(_lowerCamelCase     ,				_lowerCamelCase     ,				_lowerCamelCase		)

  for step in tqdm(range(_lowerCamelCase		)		):
    a__  =torch.zeros((1, context_len)     ,				dtype=torch.long     ,				device=_lowerCamelCase		)
    a__  =random.choice(_lowerCamelCase		)
    a__  =random.randint(0     ,				len(story[0]		) - context_len - 1		)
    a__  =story[0][start : start + context_len]
    lm_optimizer.zero_grad()
    a__  =model(_lowerCamelCase     ,				labels=_lowerCamelCase		)
    a__  =outputs[0]
    a__  =compute_perplexity(_lowerCamelCase     ,				_lowerCamelCase     ,				_lowerCamelCase		)
    model.train()
    lm_loss.backward()
    # Do LM backprop
    torch.nn.utils.clip_grad_norm_(model.parameters()     ,				3.0		)
    lm_optimizer.step()
    lm_scheduler.step()  # Update learning rate schedule

    # Compute perplexity after back-propagating on the selected context
    a__  =compute_perplexity(_lowerCamelCase     ,				_lowerCamelCase     ,				_lowerCamelCase		)

    # Periodically save the stored (X, IG(X)) pairs
    if step % 10_00 == 0 and step > 1:
      intermittent_save(_lowerCamelCase     ,				_lowerCamelCase     ,				_lowerCamelCase     ,				_lowerCamelCase		)

    # Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
    a__  =recopy_model(_lowerCamelCase     ,				_lowerCamelCase     ,				_lowerCamelCase		)

    past_perps.append(past_perp.item()		)
    real_perps.append(orig_perp - real_perp.item()		)
    contexts.append(np.array(context.cpu()		)		)

  intermittent_save(_lowerCamelCase     ,				_lowerCamelCase     ,				_lowerCamelCase     ,				_lowerCamelCase		)




def       UpperCAmelCase__    (      _A				:			Tuple     ,				_A				:			Tuple="data/tokenized_stories_train_wikitext103.jbl"     ,				_A				:			int=1_00     ,				_A				:			Any=10_26     ,				_A				:			List[str]=True		):





  '''simple docstring'''







  a__  =joblib.load(_lowerCamelCase		)
  print('''data loaded'''		)
  a__  =[]
  if trim:
    for i, example in enumerate(_lowerCamelCase		):
      if len(example[0]		) > min_len:
        a__  =random.randint(0     ,				len(example[0]		) - context_len - 1		)
        objective_set.append(example[0, start : start + context_len]		)
      if len(_lowerCamelCase		) >= number:
        break
    a__  =[]
    for j in range(i + 1     ,				len(_lowerCamelCase		)		):
      if len(data[j][0]		) > min_len:
        train_data.append(data[j]		)
  else:
    a__  =data[0:number]
    a__  =data[number:]

  joblib.dump(_lowerCamelCase     ,				'''objective_set.jbl'''		)
  print('''objective set saved'''		)
  return train_data, objective_set




def       UpperCAmelCase__    (      _A				:			Optional[Any]     ,				_A				:			List[str]     ,				_A				:			Optional[int]     ,				_A				:			str     ,				_A				:			Any=50     ,				_A				:			str="secondary_learner.pt"		):





  '''simple docstring'''







  a__  =torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''		)
  # We will use the first 512 pairs from our dataset as a test set for
  # our secondary learner and the rest to train
  a__  =train_dataset[:5_12]
  a__  =train_dataset[5_12:]
  a__  =DataLoader(_lowerCamelCase     ,				shuffle=_lowerCamelCase     ,				batch_size=_lowerCamelCase		)
  a__  =DataLoader(_lowerCamelCase     ,				shuffle=_lowerCamelCase     ,				batch_size=_lowerCamelCase		)

  # secondary learner model set up
  a__  =nn.MSELoss()
  a__  =nn.MSELoss(reduction='''sum'''		)
  secondary_learner.to(_lowerCamelCase		)
  a__  =torch.optim.Adam(secondary_learner.parameters()     ,				lr=0.00_001		)
  secondary_learner.train()

  # TODO in original code this is written as number of actual batches seen
  # not number of items seen but other places it is number of items instead.
  # improve consistency! changed this to epochs for clarity
  a__  =float('''inf'''		)
  # Iterate through batches until we've used max_steps batches
  for epoch in range(int(_lowerCamelCase		)		):
    a__  =0.0
    secondary_learner.train()
    for step, batch in enumerate(_lowerCamelCase		):
      a__  =batch[0].to(_lowerCamelCase		)
      a__  =batch[1].to(_lowerCamelCase		)
      a__  =secondary_learner(_lowerCamelCase		)
      q_optimizer.zero_grad()
      a__  =loss(_lowerCamelCase     ,				real_q.float()		)
      q_loss.backward()
      q_optimizer.step()
      tr_q_loss += q_loss.item()

      # model trains fairly quickly so we won't wait for a full epoch
      # eval is triggered at eval_freq and end of epochs
      if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(_lowerCamelCase		)):
        a__  =tr_q_loss / (step + 1)

        secondary_learner.eval()
        a__  =0.0
        a__  =0.0
        a__  =[]
        a__  =[]
        # Compute performance of the secondary learner after this batch
        for stepa, batcha in enumerate(_lowerCamelCase		):
          a__  =batcha[0].to(_lowerCamelCase		)
          a__  =batcha[1].to(_lowerCamelCase		)
          a__  =secondary_learner(_lowerCamelCase		)
          q_lossa += test_loss(_lowerCamelCase     ,				_lowerCamelCase		).item()
          sum_qa += torch.sum(_lowerCamelCase		).item()
          for ei, i in enumerate(predicted_qa.cpu().detach().numpy()		):
            predicted.append(i.item()		)
          for ei, i in enumerate(real_qa.cpu().detach().numpy()		):
            actual.append(i.item()		)

        q_lossa /= len(_lowerCamelCase		)
        print(
            '''Epoch: '''     ,				_lowerCamelCase     ,				'''step: '''     ,				_lowerCamelCase     ,				'''Avg. q:'''     ,				sum_qa / len(_lowerCamelCase		)     ,				'''Train Loss: '''     ,				_lowerCamelCase     ,				'''Test Loss: '''     ,				_lowerCamelCase     ,				)
        if q_lossa < best_test_loss:
          joblib.dump((predicted, actual)     ,				'''pred_vs_actual.jbl'''		)
          torch.save(secondary_learner.state_dict()     ,				_lowerCamelCase		)
          a__  =q_lossa

      secondary_learner.train()
  return secondary_learner







class 						__magic_name__      (							nn.Module							):

   '''simple docstring'''




   def __init__(    self,   lowercase_	)							->   str:






     """simple docstring"""






     super(snake_case_,   self	).__init__()
     a__  =model.transformer.wte
     a__  =copy.deepcopy(model.transformer.wte.weight	)
     a__  =nn.Convad(self.embeddings.weight.size(1	),   256,   3,   padding=1	)
     a__  =nn.Sequential(nn.Linear(256,   32	),   nn.Dropout(p=0.1	),   nn.Linear(32,   32	),   nn.Linear(32,   1	)	)



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   Optional[Any]:






     """simple docstring"""






     a__  =torch.max(self.conv(self.embeddings(snake_case_	).squeeze(1	).transpose(1,   2	)	),   2	)[0]
     a__  =self.fc(snake_case_	)
     return qs.squeeze(1	)



   @classmethod
   def 			_UpperCAmelCase			(    cls,   lowercase_,   lowercase_	)							->   Any:






     """simple docstring"""






     a__  =cls(snake_case_	)  # this calls __init__
     a__  =torch.load(snake_case_	)
     secondary_learner.load_state_dict(snake_case_	)
     a__  =model.transformer.wte
     a__  =copy.deepcopy(model.transformer.wte.weight	)
     return secondary_learner



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 						__magic_name__      :

   '''simple docstring'''




   def __init__(    self,   lowercase_,   lowercase_=36000,   lowercase_=13,   lowercase_=7,   lowercase_=True,   lowercase_=32,   lowercase_=42,   lowercase_=5,   lowercase_=2,   lowercase_=4,   lowercase_=2,   lowercase_=32,   lowercase_=80,   lowercase_=33,   lowercase_=0.0,   lowercase_=1E-6,   lowercase_=100,   lowercase_=0.0,   )							->   Optional[int]:






     """simple docstring"""






     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 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






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

     a__  =self.get_config()

     return (config, input_ids)



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






     """simple docstring"""






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

     a__  =self.get_config()

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



   def 			_UpperCAmelCase			(    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 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   )							->   Tuple:






     """simple docstring"""






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







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

   '''simple docstring'''




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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   Tuple:






     """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 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     GPTSanJapaneseConfig()



   def 			_UpperCAmelCase			(    self	)							->   str:






     """simple docstring"""






     a__  =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	)							->   int:






     """simple docstring"""






     super().test_model_parallelism()



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






     """simple docstring"""






     pass







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

   '''simple docstring'''




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



   def 			_UpperCAmelCase			(    self	)							->   int:






     """simple docstring"""






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



   def 			_UpperCAmelCase			(    self	)							->   Dict:






     """simple docstring"""






     GPTSanJapaneseConfig()



   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     a__  =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	)							->   Dict:






     """simple docstring"""






     super().test_model_parallelism()



   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
   def 			_UpperCAmelCase			(    self	)							->   Optional[int]:






     """simple docstring"""






     pass



   @slow
   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     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(lowercase_	)
     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(lowercase_	).flatten()
     a__  =output_logits[0, :, :20].flatten()

     def check(lowercase_,   lowercase_,   lowercase_=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	)							->   Optional[int]:






     """simple docstring"""






     a__  =GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     a__  =GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     model.to(lowercase_	)

     # 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(lowercase_,   return_tensors='''pt''',   padding=lowercase_	)
     a__  =inputs['''input_ids'''].to(lowercase_	)

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

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

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

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

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

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

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



   @tooslow
   def 			_UpperCAmelCase			(    self	)							->   List[Any]:






     """simple docstring"""






     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(lowercase_,   return_tensors='''pt'''	)
       a__  =model(lowercase_	)
       a__  =outputs.logits.detach().cpu().numpy()[0]
       a__  =np.argmax(output_logits[-1]	)
       self.assertEqual(lowercase_,   lowercase_	)



   @slow
   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     a__  =GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     a__  =GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     model.to(lowercase_	)

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

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

     # 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=lowercase_,   spout=spouts[0],   max_new_tokens=20,   generation_config=lowercase_,   )

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

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

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

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



   @slow
   def 			_UpperCAmelCase			(    self	)							->   str:






     """simple docstring"""






     a__  =GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     a__  =GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
     model.to(lowercase_	)

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

     a__  =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_,   )

     a__  =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_,   )

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

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