
'''simple docstring'''



import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         ErnieMForInformationExtraction,
         ErnieMForMultipleChoice,
         ErnieMForQuestionAnswering,
         ErnieMForSequenceClassification,
         ErnieMForTokenClassification,
         ErnieMModel,
     )
     from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST

class 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__    ,							A__=13    ,							A__=7    ,							A__=True    ,							A__=True    ,							A__=True    ,							A__=99    ,							A__=32    ,							A__=5    ,							A__=4    ,							A__=37    ,							A__="gelu"    ,							A__=0.1    ,							A__=0.1    ,							A__=5_12    ,							A__=16    ,							A__=2    ,							A__=0.02    ,							A__=3    ,							A__=4    ,							A__=None    ,							)      ->     int:
          _SCREAMING_SNAKE_CASE       	=			parent
          _SCREAMING_SNAKE_CASE       	=			batch_size
          _SCREAMING_SNAKE_CASE       	=			seq_length
          _SCREAMING_SNAKE_CASE       	=			is_training
          _SCREAMING_SNAKE_CASE       	=			use_input_mask
          _SCREAMING_SNAKE_CASE       	=			use_labels
          _SCREAMING_SNAKE_CASE       	=			vocab_size
          _SCREAMING_SNAKE_CASE       	=			hidden_size
          _SCREAMING_SNAKE_CASE       	=			num_hidden_layers
          _SCREAMING_SNAKE_CASE       	=			num_attention_heads
          _SCREAMING_SNAKE_CASE       	=			intermediate_size
          _SCREAMING_SNAKE_CASE       	=			hidden_act
          _SCREAMING_SNAKE_CASE       	=			hidden_dropout_prob
          _SCREAMING_SNAKE_CASE       	=			attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE       	=			max_position_embeddings
          _SCREAMING_SNAKE_CASE       	=			type_vocab_size
          _SCREAMING_SNAKE_CASE       	=			type_sequence_label_size
          _SCREAMING_SNAKE_CASE       	=			initializer_range
          _SCREAMING_SNAKE_CASE       	=			num_labels
          _SCREAMING_SNAKE_CASE       	=			num_choices
          _SCREAMING_SNAKE_CASE       	=			scope






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

          _SCREAMING_SNAKE_CASE       	=			None
          if self.use_input_mask:
             _SCREAMING_SNAKE_CASE       	=			random_attention_mask([self.batch_size, self.seq_length] )

          _SCREAMING_SNAKE_CASE       	=			None
          _SCREAMING_SNAKE_CASE       	=			None
          _SCREAMING_SNAKE_CASE       	=			None
          if self.use_labels:
             _SCREAMING_SNAKE_CASE       	=			ids_tensor([self.batch_size]    ,							self.type_sequence_label_size )
             _SCREAMING_SNAKE_CASE       	=			ids_tensor([self.batch_size, self.seq_length]    ,							self.num_labels )
             _SCREAMING_SNAKE_CASE       	=			ids_tensor([self.batch_size]    ,							self.num_choices )

          _SCREAMING_SNAKE_CASE       	=			self.get_config()

          return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels






       def     UpperCamelCase			(							self )      ->     int:
          _SCREAMING_SNAKE_CASE       	=			ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size )

          _SCREAMING_SNAKE_CASE       	=			None
          if self.use_input_mask:
             _SCREAMING_SNAKE_CASE       	=			random_attention_mask([self.batch_size, self.seq_length] )
          _SCREAMING_SNAKE_CASE       	=			self.get_config()

          return config, input_ids, input_mask






       def     UpperCamelCase			(							self )      ->     Tuple:
          return ErnieMConfig(
              vocab_size=self.vocab_size    ,							hidden_size=self.hidden_size    ,							num_hidden_layers=self.num_hidden_layers    ,							num_attention_heads=self.num_attention_heads    ,							intermediate_size=self.intermediate_size    ,							hidden_act=self.hidden_act    ,							hidden_dropout_prob=self.hidden_dropout_prob    ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,							max_position_embeddings=self.max_position_embeddings    ,							type_vocab_size=self.type_vocab_size    ,							initializer_range=self.initializer_range    ,							)






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__    ,							A__    ,							A__ )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			ErnieMModel(config=A__ )
          model.to(A__ )
          model.eval()
          _SCREAMING_SNAKE_CASE       	=			model(A__    ,							return_dict=A__ )
          self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size) )






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__    ,							A__    ,							A__ )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			ErnieMForQuestionAnswering(config=A__ )
          model.to(A__ )
          model.eval()
          _SCREAMING_SNAKE_CASE       	=			model(
              A__    ,							attention_mask=A__    ,							start_positions=A__    ,							end_positions=A__    ,							)
          self.parent.assertEqual(result.start_logits.shape    ,							(self.batch_size, self.seq_length) )
          self.parent.assertEqual(result.end_logits.shape    ,							(self.batch_size, self.seq_length) )






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__    ,							A__    ,							A__ )      ->     Any:
          _SCREAMING_SNAKE_CASE       	=			ErnieMForInformationExtraction(config=A__ )
          model.to(A__ )
          model.eval()
          _SCREAMING_SNAKE_CASE       	=			torch.ones_like(A__    ,							dtype=torch.floataa )
          _SCREAMING_SNAKE_CASE       	=			model(
              A__    ,							attention_mask=A__    ,							start_positions=A__    ,							end_positions=A__    ,							)
          self.parent.assertEqual(result.start_logits.shape    ,							(self.batch_size, self.seq_length) )
          self.parent.assertEqual(result.end_logits.shape    ,							(self.batch_size, self.seq_length) )






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__    ,							A__    ,							A__ )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			self.num_labels
          _SCREAMING_SNAKE_CASE       	=			ErnieMForSequenceClassification(A__ )
          model.to(A__ )
          model.eval()
          _SCREAMING_SNAKE_CASE       	=			model(A__    ,							attention_mask=A__    ,							labels=A__ )
          self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels) )






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__    ,							A__    ,							A__ )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			self.num_labels
          _SCREAMING_SNAKE_CASE       	=			ErnieMForTokenClassification(config=A__ )
          model.to(A__ )
          model.eval()
          input_ids.to(A__ )
          input_mask.to(A__ )
          token_labels.to(A__ )

          _SCREAMING_SNAKE_CASE       	=			model(A__    ,							attention_mask=A__    ,							labels=A__ )

          self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.num_labels) )






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__    ,							A__    ,							A__ )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			self.num_choices
          _SCREAMING_SNAKE_CASE       	=			ErnieMForMultipleChoice(config=A__ )
          model.to(A__ )
          model.eval()
          _SCREAMING_SNAKE_CASE       	=			input_ids.unsqueeze(1 ).expand(-1    ,							self.num_choices    ,							-1 ).contiguous()
          _SCREAMING_SNAKE_CASE       	=			input_mask.unsqueeze(1 ).expand(-1    ,							self.num_choices    ,							-1 ).contiguous()
          _SCREAMING_SNAKE_CASE       	=			model(
              A__    ,							attention_mask=A__    ,							labels=A__    ,							)
          self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_choices) )






       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			self.prepare_config_and_inputs()
          (
              (
              _SCREAMING_SNAKE_CASE
          )	,							(
              _SCREAMING_SNAKE_CASE
          )	,							(
              _SCREAMING_SNAKE_CASE
          )	,							(
              _SCREAMING_SNAKE_CASE
          )	,							(
              _SCREAMING_SNAKE_CASE
          )	,							(
              _SCREAMING_SNAKE_CASE
          )	,							
          )       	=			config_and_inputs
          _SCREAMING_SNAKE_CASE       	=			{"""input_ids""": input_ids, """attention_mask""": input_mask}
          return config, inputs_dict






@require_torch
class 				_a			(_lowerCamelCase					,   _lowerCamelCase					,   unittest.TestCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					(
           (
               ErnieMModel,
               ErnieMForMultipleChoice,
               ErnieMForQuestionAnswering,
               ErnieMForSequenceClassification,
               ErnieMForTokenClassification,
           )
           if is_torch_available()
           else ()
       )
       SCREAMING_SNAKE_CASE =					()
       SCREAMING_SNAKE_CASE =					(
           {
               'feature-extraction': ErnieMModel,
               'question-answering': ErnieMForQuestionAnswering,
               'text-classification': ErnieMForSequenceClassification,
               'token-classification': ErnieMForTokenClassification,
               'zero-shot': ErnieMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       SCREAMING_SNAKE_CASE =					False






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__    ,							A__ )      ->     str:
          if pipeline_test_casse_name == "QAPipelineTests":
             return True

          return False






       def     UpperCamelCase			(							self )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			ErnieMModelTester(self )
          _SCREAMING_SNAKE_CASE       	=			ConfigTester(self    ,							config_class=A__    ,							hidden_size=37 )






       def     UpperCamelCase			(							self )      ->     Any:
          self.config_tester.run_common_tests()






       def     UpperCamelCase			(							self )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A__ )






       def     UpperCamelCase			(							self )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
             _SCREAMING_SNAKE_CASE       	=			type
             self.model_tester.create_and_check_model(*A__ )






       def     UpperCamelCase			(							self )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*A__ )






       def     UpperCamelCase			(							self )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*A__ )






       def     UpperCamelCase			(							self )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*A__ )






       def     UpperCamelCase			(							self )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_information_extraction(*A__ )






       def     UpperCamelCase			(							self )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*A__ )






       @slow
       def     UpperCamelCase			(							self )      ->     List[str]:
          for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _SCREAMING_SNAKE_CASE       	=			ErnieMModel.from_pretrained(A__ )
             self.assertIsNotNone(A__ )






@require_torch
class 				_a			(unittest.TestCase):





       """simple docstring"""




       @slow
       def     UpperCamelCase			(							self )      ->     Tuple:
          _SCREAMING_SNAKE_CASE       	=			ErnieMModel.from_pretrained("""susnato/ernie-m-base_pytorch""" )
          model.eval()
          _SCREAMING_SNAKE_CASE       	=			torch.tensor([[0, 1, 2, 3, 4, 5]] )
          _SCREAMING_SNAKE_CASE       	=			model(A__ )[0]

          # TODO Replace vocab size
          _SCREAMING_SNAKE_CASE       	=			7_68

          _SCREAMING_SNAKE_CASE       	=			torch.Size((1, 6, hidden_size) )
          self.assertEqual(output.shape    ,							A__ )

          _SCREAMING_SNAKE_CASE       	=			torch.tensor(
              [[[-0.0012, 0.1245, -0.0214], [-0.0742, 0.0244, -0.0771], [-0.0333, 0.1164, -0.1554]]] )

          self.assertTrue(torch.allclose(output[:, :3, :3]    ,							A__    ,							atol=1E-3 ) )








'''simple docstring'''



# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
UpperCamelCase__ :						str    							=					1E-10
UpperCamelCase__ :						Any    							=					"This is incredible! I love it, this is the best chicken I have ever had."
UpperCamelCase__ :						Union[str, Any]    							=					100

class 				_a			(nn.Module):





       """simple docstring"""




       def __init__(							self    ,							A__    ,							A__="gpt2-medium"    ,							A__=False    ,							A__="cpu" )      ->     Any:
          super().__init__()
          _SCREAMING_SNAKE_CASE       	=			GPTaTokenizer.from_pretrained(A__ )
          _SCREAMING_SNAKE_CASE       	=			GPTaLMHeadModel.from_pretrained(A__ )
          _SCREAMING_SNAKE_CASE       	=			self.encoder.transformer.config.hidden_size
          _SCREAMING_SNAKE_CASE       	=			ClassificationHead(class_size=A__    ,							embed_size=self.embed_size )
          _SCREAMING_SNAKE_CASE       	=			cached_mode
          _SCREAMING_SNAKE_CASE       	=			device






       def     UpperCamelCase			(							self )      ->     str:
          return self.classifier_head






       def     UpperCamelCase			(							self )      ->     List[Any]:
          for param in self.encoder.parameters():
             _SCREAMING_SNAKE_CASE       	=			False
          self.classifier_head.train()






       def     UpperCamelCase			(							self    ,							A__ )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			x.ne(0 ).unsqueeze(2 ).repeat(1    ,							1    ,							self.embed_size ).float().to(self.device ).detach()
          _SCREAMING_SNAKE_CASE       	=			self.encoder.transformer(A__ )["""last_hidden_state"""]
          _SCREAMING_SNAKE_CASE       	=			hidden * mask
          _SCREAMING_SNAKE_CASE       	=			torch.sum(A__    ,							dim=1 ) / (torch.sum(A__    ,							dim=1 ).detach() + EPSILON)
          return avg_hidden






       def     UpperCamelCase			(							self    ,							A__ )      ->     Any:
          if self.cached_mode:
             _SCREAMING_SNAKE_CASE       	=			x.to(self.device )
          else:
             _SCREAMING_SNAKE_CASE       	=			self.avg_representation(x.to(self.device ) )

          _SCREAMING_SNAKE_CASE       	=			self.classifier_head(A__ )
          _SCREAMING_SNAKE_CASE       	=			nn.functional.log_softmax(A__    ,							dim=-1 )

          return probs






class 				_a			(data.Dataset):





       """simple docstring"""




       def __init__(							self    ,							A__    ,							A__ )      ->     Tuple:
          _SCREAMING_SNAKE_CASE       	=			X
          _SCREAMING_SNAKE_CASE       	=			y






       def __len__(							self )      ->     Any:
          return len(self.X )






       def __getitem__(							self    ,							A__ )      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			{}
          _SCREAMING_SNAKE_CASE       	=			self.X[index]
          _SCREAMING_SNAKE_CASE       	=			self.y[index]
          return data






def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       Tuple:




   """simple docstring"""
   def pad_sequences(SCREAMING_SNAKE_CASE_  ):
      _SCREAMING_SNAKE_CASE       	=			[len(SCREAMING_SNAKE_CASE_  ) for seq in sequences]

      _SCREAMING_SNAKE_CASE       	=			torch.zeros(len(SCREAMING_SNAKE_CASE_  )      , max(SCREAMING_SNAKE_CASE_  )  ).long()  # padding value = 0

      for i, seq in enumerate(SCREAMING_SNAKE_CASE_  ):
         _SCREAMING_SNAKE_CASE       	=			lengths[i]
         _SCREAMING_SNAKE_CASE       	=			seq[:end]

      return padded_sequences, lengths

   _SCREAMING_SNAKE_CASE       	=			{}
   for key in data[0].keys():
      _SCREAMING_SNAKE_CASE       	=			[d[key] for d in data]

   _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			pad_sequences(item_info["""X"""]  )
   _SCREAMING_SNAKE_CASE       	=			torch.tensor(item_info["""y"""]      , dtype=torch.long  )

   return x_batch, y_batch
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       Optional[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			{}
   for key in data[0].keys():
      _SCREAMING_SNAKE_CASE       	=			[d[key] for d in data]

   _SCREAMING_SNAKE_CASE       	=			torch.cat(item_info["""X"""]      , 0  )
   _SCREAMING_SNAKE_CASE       	=			torch.tensor(item_info["""y"""]      , dtype=torch.long  )

   return x_batch, y_batch
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=0      , SCREAMING_SNAKE_CASE_=10      , SCREAMING_SNAKE_CASE_="cpu"  )       ->       Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			0
   discriminator.train_custom()
   for batch_idx, (input_t, target_t) in enumerate(SCREAMING_SNAKE_CASE_  ):
      _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			input_t.to(SCREAMING_SNAKE_CASE_  ), target_t.to(SCREAMING_SNAKE_CASE_  )

      optimizer.zero_grad()

      _SCREAMING_SNAKE_CASE       	=			discriminator(SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			nn.functional.nll_loss(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
      loss.backward(retain_graph=SCREAMING_SNAKE_CASE_  )
      optimizer.step()

      samples_so_far += len(SCREAMING_SNAKE_CASE_  )

      if batch_idx % log_interval == 0:
         print(
             """Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}""".format(
                 epoch + 1      , SCREAMING_SNAKE_CASE_      , len(data_loader.dataset  )      , 1_00 * samples_so_far / len(data_loader.dataset  )      , loss.item()      , )  )
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_="cpu"  )       ->       Any:




   """simple docstring"""
   discriminator.eval()
   _SCREAMING_SNAKE_CASE       	=			0
   _SCREAMING_SNAKE_CASE       	=			0
   with torch.no_grad():
      for input_t, target_t in data_loader:
         _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			input_t.to(SCREAMING_SNAKE_CASE_  ), target_t.to(SCREAMING_SNAKE_CASE_  )
         _SCREAMING_SNAKE_CASE       	=			discriminator(SCREAMING_SNAKE_CASE_  )
         # sum up batch loss
         test_loss += nn.functional.nll_loss(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , reduction="""sum"""  ).item()
         # get the index of the max log-probability
         _SCREAMING_SNAKE_CASE       	=			output_t.argmax(dim=1      , keepdim=SCREAMING_SNAKE_CASE_  )
         correct += pred_t.eq(target_t.view_as(SCREAMING_SNAKE_CASE_  )  ).sum().item()

   test_loss /= len(data_loader.dataset  )

   print(
       """Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)""".format(
           SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , len(data_loader.dataset  )      , 100.0 * correct / len(data_loader.dataset  )  )  )
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_="cpu"  )       ->       str:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			model.tokenizer.encode(SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			torch.tensor([input_t]      , dtype=torch.long      , device=SCREAMING_SNAKE_CASE_  )
   if cached:
      _SCREAMING_SNAKE_CASE       	=			model.avg_representation(SCREAMING_SNAKE_CASE_  )

   _SCREAMING_SNAKE_CASE       	=			model(SCREAMING_SNAKE_CASE_  ).data.cpu().numpy().flatten().tolist()
   print("""Input sentence:"""      , SCREAMING_SNAKE_CASE_  )
   print(
       """Predictions:"""      , """, """.join("""{}: {:.4f}""".format(SCREAMING_SNAKE_CASE_      , math.exp(SCREAMING_SNAKE_CASE_  )  ) for c, log_prob in zip(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )  )      , )
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_="cpu"  )       ->       List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			torch.utils.data.DataLoader(dataset=SCREAMING_SNAKE_CASE_      , batch_size=SCREAMING_SNAKE_CASE_      , collate_fn=SCREAMING_SNAKE_CASE_  )

   _SCREAMING_SNAKE_CASE       	=			[]
   _SCREAMING_SNAKE_CASE       	=			[]
   for batch_idx, (x, y) in enumerate(tqdm(SCREAMING_SNAKE_CASE_      , ascii=SCREAMING_SNAKE_CASE_  )  ):
      with torch.no_grad():
         _SCREAMING_SNAKE_CASE       	=			x.to(SCREAMING_SNAKE_CASE_  )
         _SCREAMING_SNAKE_CASE       	=			discriminator.avg_representation(SCREAMING_SNAKE_CASE_  ).cpu().detach()
         _SCREAMING_SNAKE_CASE       	=			torch.unbind(avg_rep.unsqueeze(1  )  )
         xs += avg_rep_list
         ys += y.cpu().numpy().tolist()

   _SCREAMING_SNAKE_CASE       	=			torch.utils.data.DataLoader(
       dataset=Dataset(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )      , batch_size=SCREAMING_SNAKE_CASE_      , shuffle=SCREAMING_SNAKE_CASE_      , collate_fn=SCREAMING_SNAKE_CASE_  )

   return data_loader
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=None      , SCREAMING_SNAKE_CASE_="gpt2-medium"      , SCREAMING_SNAKE_CASE_=10      , SCREAMING_SNAKE_CASE_=64      , SCREAMING_SNAKE_CASE_=10      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_=False      , SCREAMING_SNAKE_CASE_=False      , )       ->       Any:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			"""cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""

   print("""Preprocessing {} dataset...""".format(SCREAMING_SNAKE_CASE_  )  )
   _SCREAMING_SNAKE_CASE       	=			time.time()

   if dataset == "SST":
      _SCREAMING_SNAKE_CASE       	=			["""positive""", """negative""", """very positive""", """very negative""", """neutral"""]
      _SCREAMING_SNAKE_CASE       	=			{c: i for i, c in enumerate(SCREAMING_SNAKE_CASE_  )}

      _SCREAMING_SNAKE_CASE       	=			Discriminator(
          class_size=len(SCREAMING_SNAKE_CASE_  )      , pretrained_model=SCREAMING_SNAKE_CASE_      , cached_mode=SCREAMING_SNAKE_CASE_      , device=SCREAMING_SNAKE_CASE_  ).to(SCREAMING_SNAKE_CASE_  )

      _SCREAMING_SNAKE_CASE       	=			torchtext_data.Field()
      _SCREAMING_SNAKE_CASE       	=			torchtext_data.Field(sequential=SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			datasets.SST.splits(
          SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , fine_grained=SCREAMING_SNAKE_CASE_      , train_subtrees=SCREAMING_SNAKE_CASE_      , )

      _SCREAMING_SNAKE_CASE       	=			[]
      _SCREAMING_SNAKE_CASE       	=			[]
      for i in trange(len(SCREAMING_SNAKE_CASE_  )      , ascii=SCREAMING_SNAKE_CASE_  ):
         _SCREAMING_SNAKE_CASE       	=			TreebankWordDetokenizer().detokenize(vars(train_data[i]  )["""text"""]  )
         _SCREAMING_SNAKE_CASE       	=			discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE_  )
         _SCREAMING_SNAKE_CASE       	=			torch.tensor([5_02_56] + seq      , device=SCREAMING_SNAKE_CASE_      , dtype=torch.long  )
         x.append(SCREAMING_SNAKE_CASE_  )
         y.append(classaidx[vars(train_data[i]  )["""label"""]]  )
      _SCREAMING_SNAKE_CASE       	=			Dataset(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )

      _SCREAMING_SNAKE_CASE       	=			[]
      _SCREAMING_SNAKE_CASE       	=			[]
      for i in trange(len(SCREAMING_SNAKE_CASE_  )      , ascii=SCREAMING_SNAKE_CASE_  ):
         _SCREAMING_SNAKE_CASE       	=			TreebankWordDetokenizer().detokenize(vars(test_data[i]  )["""text"""]  )
         _SCREAMING_SNAKE_CASE       	=			discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE_  )
         _SCREAMING_SNAKE_CASE       	=			torch.tensor([5_02_56] + seq      , device=SCREAMING_SNAKE_CASE_      , dtype=torch.long  )
         test_x.append(SCREAMING_SNAKE_CASE_  )
         test_y.append(classaidx[vars(test_data[i]  )["""label"""]]  )
      _SCREAMING_SNAKE_CASE       	=			Dataset(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )

      _SCREAMING_SNAKE_CASE       	=			{
          """class_size""": len(SCREAMING_SNAKE_CASE_  ),
          """embed_size""": discriminator.embed_size,
          """pretrained_model""": pretrained_model,
          """class_vocab""": classaidx,
          """default_class""": 2,
      }

   elif dataset == "clickbait":
      _SCREAMING_SNAKE_CASE       	=			["""non_clickbait""", """clickbait"""]
      _SCREAMING_SNAKE_CASE       	=			{c: i for i, c in enumerate(SCREAMING_SNAKE_CASE_  )}

      _SCREAMING_SNAKE_CASE       	=			Discriminator(
          class_size=len(SCREAMING_SNAKE_CASE_  )      , pretrained_model=SCREAMING_SNAKE_CASE_      , cached_mode=SCREAMING_SNAKE_CASE_      , device=SCREAMING_SNAKE_CASE_  ).to(SCREAMING_SNAKE_CASE_  )

      with open("""datasets/clickbait/clickbait_train_prefix.txt"""  ) as f:
         _SCREAMING_SNAKE_CASE       	=			[]
         for i, line in enumerate(SCREAMING_SNAKE_CASE_  ):
            try:
               data.append(eval(SCREAMING_SNAKE_CASE_  )  )
            except Exception:
               print("""Error evaluating line {}: {}""".format(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )  )
               continue
      _SCREAMING_SNAKE_CASE       	=			[]
      _SCREAMING_SNAKE_CASE       	=			[]
      with open("""datasets/clickbait/clickbait_train_prefix.txt"""  ) as f:
         for i, line in enumerate(tqdm(SCREAMING_SNAKE_CASE_      , ascii=SCREAMING_SNAKE_CASE_  )  ):
            try:
               _SCREAMING_SNAKE_CASE       	=			eval(SCREAMING_SNAKE_CASE_  )
               _SCREAMING_SNAKE_CASE       	=			discriminator.tokenizer.encode(d["""text"""]  )

               if len(SCREAMING_SNAKE_CASE_  ) < max_length_seq:
                  _SCREAMING_SNAKE_CASE       	=			torch.tensor([5_02_56] + seq      , device=SCREAMING_SNAKE_CASE_      , dtype=torch.long  )
               else:
                  print("""Line {} is longer than maximum length {}""".format(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )  )
                  continue
               x.append(SCREAMING_SNAKE_CASE_  )
               y.append(d["""label"""]  )
            except Exception:
               print("""Error evaluating / tokenizing line {}, skipping it""".format(SCREAMING_SNAKE_CASE_  )  )
               pass

      _SCREAMING_SNAKE_CASE       	=			Dataset(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			int(0.9 * len(SCREAMING_SNAKE_CASE_  )  )
      _SCREAMING_SNAKE_CASE       	=			len(SCREAMING_SNAKE_CASE_  ) - train_size
      _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			torch.utils.data.random_split(SCREAMING_SNAKE_CASE_      , [train_size, test_size]  )

      _SCREAMING_SNAKE_CASE       	=			{
          """class_size""": len(SCREAMING_SNAKE_CASE_  ),
          """embed_size""": discriminator.embed_size,
          """pretrained_model""": pretrained_model,
          """class_vocab""": classaidx,
          """default_class""": 1,
      }

   elif dataset == "toxic":
      _SCREAMING_SNAKE_CASE       	=			["""non_toxic""", """toxic"""]
      _SCREAMING_SNAKE_CASE       	=			{c: i for i, c in enumerate(SCREAMING_SNAKE_CASE_  )}

      _SCREAMING_SNAKE_CASE       	=			Discriminator(
          class_size=len(SCREAMING_SNAKE_CASE_  )      , pretrained_model=SCREAMING_SNAKE_CASE_      , cached_mode=SCREAMING_SNAKE_CASE_      , device=SCREAMING_SNAKE_CASE_  ).to(SCREAMING_SNAKE_CASE_  )

      _SCREAMING_SNAKE_CASE       	=			[]
      _SCREAMING_SNAKE_CASE       	=			[]
      with open("""datasets/toxic/toxic_train.txt"""  ) as f:
         for i, line in enumerate(tqdm(SCREAMING_SNAKE_CASE_      , ascii=SCREAMING_SNAKE_CASE_  )  ):
            try:
               _SCREAMING_SNAKE_CASE       	=			eval(SCREAMING_SNAKE_CASE_  )
               _SCREAMING_SNAKE_CASE       	=			discriminator.tokenizer.encode(d["""text"""]  )

               if len(SCREAMING_SNAKE_CASE_  ) < max_length_seq:
                  _SCREAMING_SNAKE_CASE       	=			torch.tensor([5_02_56] + seq      , device=SCREAMING_SNAKE_CASE_      , dtype=torch.long  )
               else:
                  print("""Line {} is longer than maximum length {}""".format(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )  )
                  continue
               x.append(SCREAMING_SNAKE_CASE_  )
               y.append(int(np.sum(d["""label"""]  ) > 0  )  )
            except Exception:
               print("""Error evaluating / tokenizing line {}, skipping it""".format(SCREAMING_SNAKE_CASE_  )  )
               pass

      _SCREAMING_SNAKE_CASE       	=			Dataset(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			int(0.9 * len(SCREAMING_SNAKE_CASE_  )  )
      _SCREAMING_SNAKE_CASE       	=			len(SCREAMING_SNAKE_CASE_  ) - train_size
      _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			torch.utils.data.random_split(SCREAMING_SNAKE_CASE_      , [train_size, test_size]  )

      _SCREAMING_SNAKE_CASE       	=			{
          """class_size""": len(SCREAMING_SNAKE_CASE_  ),
          """embed_size""": discriminator.embed_size,
          """pretrained_model""": pretrained_model,
          """class_vocab""": classaidx,
          """default_class""": 0,
      }

   else:  # if dataset == "generic":
      # This assumes the input dataset is a TSV with the following structure:
      # class \t text

      if dataset_fp is None:
         raise ValueError("""When generic dataset is selected, dataset_fp needs to be specified aswell."""  )

      _SCREAMING_SNAKE_CASE       	=			set()
      with open(SCREAMING_SNAKE_CASE_  ) as f:
         _SCREAMING_SNAKE_CASE       	=			csv.reader(SCREAMING_SNAKE_CASE_      , delimiter="""\t"""  )
         for row in tqdm(SCREAMING_SNAKE_CASE_      , ascii=SCREAMING_SNAKE_CASE_  ):
            if row:
               classes.add(row[0]  )

      _SCREAMING_SNAKE_CASE       	=			sorted(SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			{c: i for i, c in enumerate(SCREAMING_SNAKE_CASE_  )}

      _SCREAMING_SNAKE_CASE       	=			Discriminator(
          class_size=len(SCREAMING_SNAKE_CASE_  )      , pretrained_model=SCREAMING_SNAKE_CASE_      , cached_mode=SCREAMING_SNAKE_CASE_      , device=SCREAMING_SNAKE_CASE_  ).to(SCREAMING_SNAKE_CASE_  )

      _SCREAMING_SNAKE_CASE       	=			[]
      _SCREAMING_SNAKE_CASE       	=			[]
      with open(SCREAMING_SNAKE_CASE_  ) as f:
         _SCREAMING_SNAKE_CASE       	=			csv.reader(SCREAMING_SNAKE_CASE_      , delimiter="""\t"""  )
         for i, row in enumerate(tqdm(SCREAMING_SNAKE_CASE_      , ascii=SCREAMING_SNAKE_CASE_  )  ):
            if row:
               _SCREAMING_SNAKE_CASE       	=			row[0]
               _SCREAMING_SNAKE_CASE       	=			row[1]

               try:
                  _SCREAMING_SNAKE_CASE       	=			discriminator.tokenizer.encode(SCREAMING_SNAKE_CASE_  )
                  if len(SCREAMING_SNAKE_CASE_  ) < max_length_seq:
                     _SCREAMING_SNAKE_CASE       	=			torch.tensor([5_02_56] + seq      , device=SCREAMING_SNAKE_CASE_      , dtype=torch.long  )

                  else:
                     print("""Line {} is longer than maximum length {}""".format(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )  )
                     continue

                  x.append(SCREAMING_SNAKE_CASE_  )
                  y.append(classaidx[label]  )

               except Exception:
                  print("""Error tokenizing line {}, skipping it""".format(SCREAMING_SNAKE_CASE_  )  )
                  pass

      _SCREAMING_SNAKE_CASE       	=			Dataset(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			int(0.9 * len(SCREAMING_SNAKE_CASE_  )  )
      _SCREAMING_SNAKE_CASE       	=			len(SCREAMING_SNAKE_CASE_  ) - train_size
      _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			torch.utils.data.random_split(SCREAMING_SNAKE_CASE_      , [train_size, test_size]  )

      _SCREAMING_SNAKE_CASE       	=			{
          """class_size""": len(SCREAMING_SNAKE_CASE_  ),
          """embed_size""": discriminator.embed_size,
          """pretrained_model""": pretrained_model,
          """class_vocab""": classaidx,
          """default_class""": 0,
      }

   _SCREAMING_SNAKE_CASE       	=			time.time()
   print("""Preprocessed {} data points""".format(len(SCREAMING_SNAKE_CASE_  ) + len(SCREAMING_SNAKE_CASE_  )  )  )
   print("""Data preprocessing took: {:.3f}s""".format(end - start  )  )

   if cached:
      print("""Building representation cache..."""  )

      _SCREAMING_SNAKE_CASE       	=			time.time()

      _SCREAMING_SNAKE_CASE       	=			get_cached_data_loader(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , shuffle=SCREAMING_SNAKE_CASE_      , device=SCREAMING_SNAKE_CASE_  )

      _SCREAMING_SNAKE_CASE       	=			get_cached_data_loader(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , device=SCREAMING_SNAKE_CASE_  )

      _SCREAMING_SNAKE_CASE       	=			time.time()
      print("""Building representation cache took: {:.3f}s""".format(end - start  )  )

   else:
      _SCREAMING_SNAKE_CASE       	=			torch.utils.data.DataLoader(
          dataset=SCREAMING_SNAKE_CASE_      , batch_size=SCREAMING_SNAKE_CASE_      , shuffle=SCREAMING_SNAKE_CASE_      , collate_fn=SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			torch.utils.data.DataLoader(dataset=SCREAMING_SNAKE_CASE_      , batch_size=SCREAMING_SNAKE_CASE_      , collate_fn=SCREAMING_SNAKE_CASE_  )

   if save_model:
      with open("""{}_classifier_head_meta.json""".format(SCREAMING_SNAKE_CASE_  )      , """w"""  ) as meta_file:
         json.dump(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )

   _SCREAMING_SNAKE_CASE       	=			optim.Adam(discriminator.parameters()      , lr=0.0001  )

   for epoch in range(SCREAMING_SNAKE_CASE_  ):
      _SCREAMING_SNAKE_CASE       	=			time.time()
      print("""\nEpoch"""      , epoch + 1  )

      train_epoch(
          discriminator=SCREAMING_SNAKE_CASE_      , data_loader=SCREAMING_SNAKE_CASE_      , optimizer=SCREAMING_SNAKE_CASE_      , epoch=SCREAMING_SNAKE_CASE_      , log_interval=SCREAMING_SNAKE_CASE_      , device=SCREAMING_SNAKE_CASE_      , )
      evaluate_performance(data_loader=SCREAMING_SNAKE_CASE_      , discriminator=SCREAMING_SNAKE_CASE_      , device=SCREAMING_SNAKE_CASE_  )

      _SCREAMING_SNAKE_CASE       	=			time.time()
      print("""Epoch took: {:.3f}s""".format(end - start  )  )

      print("""\nExample prediction"""  )
      predict(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , cached=SCREAMING_SNAKE_CASE_      , device=SCREAMING_SNAKE_CASE_  )

      if save_model:
         # torch.save(discriminator.state_dict(),
         #           "{}_discriminator_{}.pt".format(
         #               args.dataset, epoch + 1
         #               ))
         torch.save(
             discriminator.get_classifier().state_dict()      , """{}_classifier_head_epoch_{}.pt""".format(SCREAMING_SNAKE_CASE_      , epoch + 1  )      , )


if __name__ == "__main__":
     UpperCamelCase__ :						int    							=					argparse.ArgumentParser(description="Train a discriminator on top of GPT-2 representations")
     parser.add_argument(
         "--dataset",
         type=str,
         default="SST",
         choices=("SST", "clickbait", "toxic", "generic"),
         help=(
             "dataset to train the discriminator on."
             "In case of generic, the dataset is expected"
             "to be a TSBV file with structure: class \\t text"
         ),
     )
     parser.add_argument(
         "--dataset_fp",
         type=str,
         default="",
         help="File path of the dataset to use. Needed only in case of generic datadset",
     )
     parser.add_argument(
         "--pretrained_model", type=str, default="gpt2-medium", help="Pretrained model to use as encoder"
     )
     parser.add_argument("--epochs", type=int, default=10, metavar="N", help="Number of training epochs")
     parser.add_argument(
         "--batch_size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
     )
     parser.add_argument(
         "--log_interval",
         type=int,
         default=10,
         metavar="N",
         help="how many batches to wait before logging training status",
     )
     parser.add_argument("--save_model", action="store_true", help="whether to save the model")
     parser.add_argument("--cached", action="store_true", help="whether to cache the input representations")
     parser.add_argument("--no_cuda", action="store_true", help="use to turn off cuda")
     UpperCamelCase__ :						List[Any]    							=					parser.parse_args()

     train_discriminator(**(vars(args)))






