




import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
       from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
       UpperCAmelCase_   							=      True
       from torch.cuda.amp import autocast


UpperCAmelCase_   							=      logging.getLogger(__name__)





@dataclass
class        lowerCamelCase__:
      UpperCAmelCase__ :      str       						=  field(
          metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
      UpperCAmelCase__ :      Optional[str]       						=  field(
          default=__lowerCamelCase					,	metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}					,	)
      UpperCAmelCase__ :      Optional[bool]       						=  field(
          default=__lowerCamelCase					,	metadata={'help': 'Whether to freeze the feature extractor layers of the model.'})
      UpperCAmelCase__ :      Optional[bool]       						=  field(
          default=__lowerCamelCase					,	metadata={'help': 'Whether to log verbose messages or not.'}					,	)




def    lowerCamelCase__			(		A__			:   ModelArguments    ,     A__			:   TrainingArguments      ):





       '''simple docstring'''



       logging.basicConfig(
           format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""    ,     datefmt="""%m/%d/%Y %H:%M:%S"""    ,     handlers=[logging.StreamHandler(sys.stdout      )]    ,     )
       __lowerCamelCase  =		logging.WARNING
       if model_args.verbose_logging:
              __lowerCamelCase  =		logging.DEBUG
       elif trainer_utils.is_main_process(training_args.local_rank      ):
              __lowerCamelCase  =		logging.INFO
       logger.setLevel(A__      )





@dataclass
class        lowerCamelCase__:
      UpperCAmelCase__ :      str       						=  field(
          default=__lowerCamelCase					,	metadata={'help': 'The name of the dataset to use (via the datasets library).'})
      UpperCAmelCase__ :      Optional[str]       						=  field(
          default=__lowerCamelCase					,	metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'})
      UpperCAmelCase__ :      Optional[str]       						=  field(
          default='train'					,	metadata={
              'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
          }					,	)
      UpperCAmelCase__ :      Optional[str]       						=  field(
          default='validation'					,	metadata={
              'help': (
                  'The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''
              )
          }					,	)
      UpperCAmelCase__ :      Optional[str]       						=  field(
          default='text'					,	metadata={'help': 'Column in the dataset that contains label (target text). Defaults to \'text\''}					,	)
      UpperCAmelCase__ :      Optional[str]       						=  field(
          default='file'					,	metadata={'help': 'Column in the dataset that contains speech file path. Defaults to \'file\''}					,	)
      UpperCAmelCase__ :      Optional[bool]       						=  field(
          default=__lowerCamelCase					,	metadata={'help': 'Resample loaded audio to target feature extractor\'s sampling rate or not.'}					,	)
      UpperCAmelCase__ :      Optional[float]       						=  field(
          default=__lowerCamelCase					,	metadata={'help': 'Filters out examples longer than specified. Defaults to no filtering.'}					,	)
      UpperCAmelCase__ :      Optional[str]       						=  field(
          default='librispeech'					,	metadata={
              'help': (
                  'Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'
                  ' \'buckwalter\'.'
              )
          }					,	)
      UpperCAmelCase__ :      bool       						=  field(
          default=__lowerCamelCase					,	metadata={'help': 'Overwrite the cached preprocessed datasets or not.'})
      UpperCAmelCase__ :      Optional[int]       						=  field(
          default=__lowerCamelCase					,	metadata={'help': 'The number of processes to use for the preprocessing.'}					,	)





@dataclass
class        lowerCamelCase__:
      UpperCAmelCase__ :      bool       						=  False
      UpperCAmelCase__ :      Optional[str]       						=  None
      UpperCAmelCase__ :      Optional[str]       						=  "|"
      UpperCAmelCase__ :      Optional[Dict[str, str]]       						=  field(default_factory=__lowerCamelCase)
      UpperCAmelCase__ :      Optional[Set[str]]       						=  field(default_factory=__lowerCamelCase)
      UpperCAmelCase__ :      Optional[Callable[[str], str]]       						=  None
      @classmethod
      def      lowerCAmelCase__		(							cls:					Dict       ,			UpperCamelCase_:					str  ):
             if name == "librispeech":
                    return cls()
             if name == "timit":
                    return cls(
                        do_lower_case=UpperCamelCase_       ,			translation_table=str.maketrans({"""-""": """ """}  )       ,			)
             if name == "buckwalter":
                    __lowerCamelCase  =		{
                        """-""": """ """,  # sometimes used to represent pauses
                        """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
                    }
                    return cls(
                        vocab_file=pathlib.Path(UpperCamelCase_  ).parent.joinpath("""vocab/buckwalter.json"""  )       ,			word_delimiter_token="""/"""       ,			translation_table=str.maketrans(UpperCamelCase_  )       ,			words_to_remove={"""sil"""}       ,			untransliterator=arabic.buckwalter.untransliterate       ,			)
             raise ValueError(F'Unsupported orthography: \'{name}\'.'  )
      def      lowerCAmelCase__		(							self:					Any       ,			UpperCamelCase_:					str  ):
             # TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
             if len(self.translation_table  ) > 0:
                    __lowerCamelCase  =		text.translate(self.translation_table  )
             if len(self.words_to_remove  ) == 0:
                    __lowerCamelCase  =		""" """.join(text.split()  )  # clean up whitespaces
             else:
                    __lowerCamelCase  =		""" """.join(w for w in text.split() if w not in self.words_to_remove  )  # and clean up whilespaces
             return text




      def      lowerCAmelCase__		(							self:					Any       ,			UpperCamelCase_:					ModelArguments  ):
             __lowerCamelCase  =		WavaVecaFeatureExtractor.from_pretrained(
                 model_args.model_name_or_path       ,			cache_dir=model_args.cache_dir  )
             if self.vocab_file:
                    __lowerCamelCase  =		WavaVecaCTCTokenizer(
                        self.vocab_file       ,			cache_dir=model_args.cache_dir       ,			do_lower_case=self.do_lower_case       ,			word_delimiter_token=self.word_delimiter_token       ,			)
             else:
                    __lowerCamelCase  =		WavaVecaCTCTokenizer.from_pretrained(
                        model_args.model_name_or_path       ,			cache_dir=model_args.cache_dir       ,			do_lower_case=self.do_lower_case       ,			word_delimiter_token=self.word_delimiter_token       ,			)
             return WavaVecaProcessor(UpperCamelCase_       ,			UpperCamelCase_  )





@dataclass
class        lowerCamelCase__:
      UpperCAmelCase__ :      WavaVecaProcessor
      UpperCAmelCase__ :      Union[bool, str]       						=  True
      UpperCAmelCase__ :      Optional[int]       						=  None
      UpperCAmelCase__ :      Optional[int]       						=  None
      UpperCAmelCase__ :      Optional[int]       						=  None
      UpperCAmelCase__ :      Optional[int]       						=  None
      def __call__(							self:					Union[str, Any]       ,			UpperCamelCase_:					List[Dict[str, Union[List[int], torch.Tensor]]]  ):
             # split inputs and labels since they have to be of different lenghts and need
             # different padding methods
             __lowerCamelCase  =		[{"""input_values""": feature["""input_values"""]} for feature in features]
             __lowerCamelCase  =		[{"""input_ids""": feature["""labels"""]} for feature in features]

             __lowerCamelCase  =		self.processor.pad(
                 UpperCamelCase_       ,			padding=self.padding       ,			max_length=self.max_length       ,			pad_to_multiple_of=self.pad_to_multiple_of       ,			return_tensors="""pt"""       ,			)
             __lowerCamelCase  =		self.processor.pad(
                 labels=UpperCamelCase_       ,			padding=self.padding       ,			max_length=self.max_length_labels       ,			pad_to_multiple_of=self.pad_to_multiple_of_labels       ,			return_tensors="""pt"""       ,			)

             # replace padding with -100 to ignore loss correctly
             __lowerCamelCase  =		labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1  )       ,			-1_00  )

             __lowerCamelCase  =		labels

             return batch





class        lowerCamelCase__(   __lowerCamelCase):
      def      lowerCAmelCase__		(							self:					Optional[Any]       ,			UpperCamelCase_:					nn.Module       ,			UpperCamelCase_:					Dict[str, Union[torch.Tensor, Any]]  ):
             model.train()
             __lowerCamelCase  =		self._prepare_inputs(UpperCamelCase_  )

             if self.use_amp:
                    with autocast():
                           __lowerCamelCase  =		self.compute_loss(UpperCamelCase_       ,			UpperCamelCase_  )
             else:
                    __lowerCamelCase  =		self.compute_loss(UpperCamelCase_       ,			UpperCamelCase_  )

             if self.args.n_gpu > 1:
                    if model.module.config.ctc_loss_reduction == "mean":
                           __lowerCamelCase  =		loss.mean()
                    elif model.module.config.ctc_loss_reduction == "sum":
                           __lowerCamelCase  =		loss.sum() / (inputs["""labels"""] >= 0).sum()
                    else:
                           raise ValueError(F'{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']'  )

             if self.args.gradient_accumulation_steps > 1:
                    __lowerCamelCase  =		loss / self.args.gradient_accumulation_steps

             if self.use_amp:
                    self.scaler.scale(UpperCamelCase_  ).backward()
             elif self.use_apex:
                    with amp.scale_loss(UpperCamelCase_       ,			self.optimizer  ) as scaled_loss:
                           scaled_loss.backward()
             elif self.deepspeed:
                    self.deepspeed.backward(UpperCamelCase_  )
             else:
                    loss.backward()

             return loss.detach()




def    lowerCamelCase__			(		):





       '''simple docstring'''



       __lowerCamelCase  =		HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase  =		parser.parse_args_into_dataclasses()
       configure_logger(A__    ,     A__      )

       __lowerCamelCase  =		Orthography.from_name(data_args.orthography.lower()      )
       __lowerCamelCase  =		orthography.create_processor(A__      )
       __lowerCamelCase  =		WavaVecaForCTC.from_pretrained(
           model_args.model_name_or_path    ,     cache_dir=model_args.cache_dir    ,     gradient_checkpointing=training_args.gradient_checkpointing    ,     vocab_size=len(processor.tokenizer      )    ,     )

       __lowerCamelCase  =		datasets.load_dataset(
           data_args.dataset_name    ,     data_args.dataset_config_name    ,     split=data_args.train_split_name      )
       __lowerCamelCase  =		datasets.load_dataset(
           data_args.dataset_name    ,     data_args.dataset_config_name    ,     split=data_args.validation_split_name      )

       __lowerCamelCase  =		datasets.load_metric("""wer"""      )
       __lowerCamelCase  =		processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
       __lowerCamelCase  =		"""""".join(t for t in processor.tokenizer.get_vocab().keys() if len(A__      ) == 1      )
       __lowerCamelCase  =		re.compile(  # remove characters not in vocabulary
           Rf'[^\s{re.escape(A__      )}]'    ,     flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0    ,     )
       __lowerCamelCase  =		[]

       def prepare_example(A__			:   Dict      ):  # TODO(elgeish) make use of multiprocessing?
              __lowerCamelCase,  __lowerCamelCase  =		librosa.load(example[data_args.speech_file_column]    ,     sr=A__      )
              if data_args.max_duration_in_seconds is not None:
                     __lowerCamelCase  =		len(example["""speech"""]      ) / example["""sampling_rate"""]
              # Normalize and clean up text; order matters!
              __lowerCamelCase  =		orthography.preprocess_for_training(example[data_args.target_text_column]      )
              __lowerCamelCase  =		vocabulary_text_cleaner.sub(""""""    ,     A__      )
              if updated_text != example[data_args.target_text_column]:
                     text_updates.append((example[data_args.target_text_column], updated_text)      )
                     __lowerCamelCase  =		updated_text
              return example

       __lowerCamelCase  =		train_dataset.map(A__    ,     remove_columns=[data_args.speech_file_column]      )
       __lowerCamelCase  =		val_dataset.map(A__    ,     remove_columns=[data_args.speech_file_column]      )

       if data_args.max_duration_in_seconds is not None:

              def filter_by_max_duration(A__			:   Union[str, Any]      ):
                     return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

              __lowerCamelCase  =		len(A__      )
              __lowerCamelCase  =		len(A__      )
              __lowerCamelCase  =		train_dataset.filter(A__    ,     remove_columns=["""duration_in_seconds"""]      )
              __lowerCamelCase  =		val_dataset.filter(A__    ,     remove_columns=["""duration_in_seconds"""]      )
              if len(A__      ) > old_train_size:
                     logger.warning(
                         f'Filtered out {len(A__      ) - old_train_size} train example(s) longer than'
                         f' {data_args.max_duration_in_seconds} second(s).'      )
              if len(A__      ) > old_val_size:
                     logger.warning(
                         f'Filtered out {len(A__      ) - old_val_size} validation example(s) longer than'
                         f' {data_args.max_duration_in_seconds} second(s).'      )
       logger.info(f'Split sizes: {len(A__      )} train and {len(A__      )} validation.'      )

       logger.warning(f'Updated {len(A__      )} transcript(s) using \'{data_args.orthography}\' orthography rules.'      )
       if logger.isEnabledFor(logging.DEBUG      ):
              for original_text, updated_text in text_updates:
                     logger.debug(f'Updated text: "{original_text}" -> "{updated_text}"'      )
       __lowerCamelCase  =		None

       def prepare_dataset(A__			:   Optional[int]      ):
              # check that all files have the correct sampling rate
              assert (
                  len(set(batch["""sampling_rate"""]      )      ) == 1
              ), f'Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'

              __lowerCamelCase  =		processor(
                  audio=batch["""speech"""]    ,     text=batch[data_args.target_text_column]    ,     sampling_rate=batch["""sampling_rate"""][0]      )
              batch.update(A__      )
              return batch

       __lowerCamelCase  =		train_dataset.map(
           A__    ,     batch_size=training_args.per_device_train_batch_size    ,     batched=A__    ,     num_proc=data_args.preprocessing_num_workers    ,     )
       __lowerCamelCase  =		val_dataset.map(
           A__    ,     batch_size=training_args.per_device_train_batch_size    ,     batched=A__    ,     num_proc=data_args.preprocessing_num_workers    ,     )

       __lowerCamelCase  =		DataCollatorCTCWithPadding(processor=A__    ,     padding=A__      )

       def compute_metrics(A__			:   List[Any]      ):
              __lowerCamelCase  =		pred.predictions
              __lowerCamelCase  =		np.argmax(A__    ,     axis=-1      )

              __lowerCamelCase  =		processor.tokenizer.pad_token_id

              __lowerCamelCase  =		processor.batch_decode(A__      )
              # we do not want to group tokens when computing the metrics
              __lowerCamelCase  =		processor.batch_decode(pred.label_ids    ,     group_tokens=A__      )
              if logger.isEnabledFor(logging.DEBUG      ):
                     for reference, predicted in zip(A__    ,     A__      ):
                            logger.debug(f'reference: "{reference}"'      )
                            logger.debug(f'predicted: "{predicted}"'      )
                            if orthography.untransliterator is not None:
                                   logger.debug(f'reference (untransliterated): "{orthography.untransliterator(A__      )}"'      )
                                   logger.debug(f'predicted (untransliterated): "{orthography.untransliterator(A__      )}"'      )

              __lowerCamelCase  =		wer_metric.compute(predictions=A__    ,     references=A__      )

              return {"wer": wer}

       if model_args.freeze_feature_extractor:
              model.freeze_feature_extractor()

       __lowerCamelCase  =		CTCTrainer(
           model=A__    ,     data_collator=A__    ,     args=A__    ,     compute_metrics=A__    ,     train_dataset=A__    ,     eval_dataset=A__    ,     tokenizer=processor.feature_extractor    ,     )

       trainer.train()


if __name__ == "__main__":
       main()







# 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_   							=      1E-10
UpperCAmelCase_   							=      'This is incredible! I love it, this is the best chicken I have ever had.'
UpperCAmelCase_   							=      100





class        lowerCamelCase__(   nn.Module):
      def __init__(							self:					Optional[Any]       ,			UpperCamelCase_:					int       ,			UpperCamelCase_:					Any="gpt2-medium"       ,			UpperCamelCase_:					Any=False       ,			UpperCamelCase_:					List[Any]="cpu"  ):
             super().__init__()
             __lowerCamelCase  =		GPTaTokenizer.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		GPTaLMHeadModel.from_pretrained(UpperCamelCase_  )
             __lowerCamelCase  =		self.encoder.transformer.config.hidden_size
             __lowerCamelCase  =		ClassificationHead(class_size=UpperCamelCase_       ,			embed_size=self.embed_size  )
             __lowerCamelCase  =		cached_mode
             __lowerCamelCase  =		device
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             return self.classifier_head
      def      lowerCAmelCase__		(							self:					int  ):
             for param in self.encoder.parameters():
                    __lowerCamelCase  =		False
             self.classifier_head.train()
      def      lowerCAmelCase__		(							self:					Optional[Any]       ,			UpperCamelCase_:					Optional[int]  ):
             __lowerCamelCase  =		x.ne(0  ).unsqueeze(2  ).repeat(1       ,			1       ,			self.embed_size  ).float().to(self.device  ).detach()
             __lowerCamelCase  =		self.encoder.transformer(UpperCamelCase_  )["""last_hidden_state"""]
             __lowerCamelCase  =		hidden * mask
             __lowerCamelCase  =		torch.sum(UpperCamelCase_       ,			dim=1  ) / (torch.sum(UpperCamelCase_       ,			dim=1  ).detach() + EPSILON)
             return avg_hidden




      def      lowerCAmelCase__		(							self:					Dict       ,			UpperCamelCase_:					Optional[int]  ):
             if self.cached_mode:
                    __lowerCamelCase  =		x.to(self.device  )
             else:
                    __lowerCamelCase  =		self.avg_representation(x.to(self.device  )  )

             __lowerCamelCase  =		self.classifier_head(UpperCamelCase_  )
             __lowerCamelCase  =		nn.functional.log_softmax(UpperCamelCase_       ,			dim=-1  )

             return probs





class        lowerCamelCase__(   data.Dataset):
      def __init__(							self:					List[Any]       ,			UpperCamelCase_:					Optional[Any]       ,			UpperCamelCase_:					Optional[int]  ):
             __lowerCamelCase  =		X
             __lowerCamelCase  =		y
      def __len__(							self:					Dict  ):
             return len(self.X  )




      def __getitem__(							self:					Dict       ,			UpperCamelCase_:					Dict  ):
             __lowerCamelCase  =		{}
             __lowerCamelCase  =		self.X[index]
             __lowerCamelCase  =		self.y[index]
             return data




def    lowerCamelCase__			(		A__			:   List[Any]      ):





       '''simple docstring'''



       def pad_sequences(A__			:   List[str]      ):
              __lowerCamelCase  =		[len(A__      ) for seq in sequences]

              __lowerCamelCase  =		torch.zeros(len(A__      )    ,     max(A__      )      ).long()  # padding value = 0

              for i, seq in enumerate(A__      ):
                     __lowerCamelCase  =		lengths[i]
                     __lowerCamelCase  =		seq[:end]

              return padded_sequences, lengths

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

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

       return x_batch, y_batch




def    lowerCamelCase__			(		A__			:   Optional[int]      ):





       '''simple docstring'''



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

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

       return x_batch, y_batch




def    lowerCamelCase__			(		A__			:   List[Any]    ,     A__			:   Optional[Any]    ,     A__			:   List[str]    ,     A__			:   int=0    ,     A__			:   Optional[Any]=10    ,     A__			:   List[str]="cpu"      ):





       '''simple docstring'''



       __lowerCamelCase  =		0
       discriminator.train_custom()
       for batch_idx, (input_t, target_t) in enumerate(A__      ):
              __lowerCamelCase,  __lowerCamelCase  =		input_t.to(A__      ), target_t.to(A__      )

              optimizer.zero_grad()

              __lowerCamelCase  =		discriminator(A__      )
              __lowerCamelCase  =		nn.functional.nll_loss(A__    ,     A__      )
              loss.backward(retain_graph=A__      )
              optimizer.step()

              samples_so_far += len(A__      )

              if batch_idx % log_interval == 0:
                     print(
                         """Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}""".format(
                             epoch + 1    ,     A__    ,     len(data_loader.dataset      )    ,     100 * samples_so_far / len(data_loader.dataset      )    ,     loss.item()    ,     )      )




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   str    ,     A__			:   int="cpu"      ):





       '''simple docstring'''



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

       test_loss /= len(data_loader.dataset      )

       print(
           """Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)""".format(
               A__    ,     A__    ,     len(data_loader.dataset      )    ,     100.0 * correct / len(data_loader.dataset      )      )      )




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   str    ,     A__			:   str    ,     A__			:   List[str]=False    ,     A__			:   Optional[Any]="cpu"      ):





       '''simple docstring'''



       __lowerCamelCase  =		model.tokenizer.encode(A__      )
       __lowerCamelCase  =		torch.tensor([input_t]    ,     dtype=torch.long    ,     device=A__      )
       if cached:
              __lowerCamelCase  =		model.avg_representation(A__      )

       __lowerCamelCase  =		model(A__      ).data.cpu().numpy().flatten().tolist()
       print("""Input sentence:"""    ,     A__      )
       print(
           """Predictions:"""    ,     """, """.join("""{}: {:.4f}""".format(A__    ,     math.exp(A__      )      ) for c, log_prob in zip(A__    ,     A__      )      )    ,     )




def    lowerCamelCase__			(		A__			:   Tuple    ,     A__			:   Any    ,     A__			:   Tuple    ,     A__			:   List[str]=False    ,     A__			:   Union[str, Any]="cpu"      ):





       '''simple docstring'''



       __lowerCamelCase  =		torch.utils.data.DataLoader(dataset=A__    ,     batch_size=A__    ,     collate_fn=A__      )

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

       __lowerCamelCase  =		torch.utils.data.DataLoader(
           dataset=Dataset(A__    ,     A__      )    ,     batch_size=A__    ,     shuffle=A__    ,     collate_fn=A__      )

       return data_loader




def    lowerCamelCase__			(		A__			:   List[Any]    ,     A__			:   Union[str, Any]=None    ,     A__			:   List[str]="gpt2-medium"    ,     A__			:   Union[str, Any]=10    ,     A__			:   str=64    ,     A__			:   str=10    ,     A__			:   Tuple=False    ,     A__			:   Optional[int]=False    ,     A__			:   str=False    ,     ):





       '''simple docstring'''



       __lowerCamelCase  =		"""cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""

       print("""Preprocessing {} dataset...""".format(A__      )      )
       __lowerCamelCase  =		time.time()

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

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

              __lowerCamelCase  =		torchtext_data.Field()
              __lowerCamelCase  =		torchtext_data.Field(sequential=A__      )
              __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase  =		datasets.SST.splits(
                  A__    ,     A__    ,     fine_grained=A__    ,     train_subtrees=A__    ,     )

              __lowerCamelCase  =		[]
              __lowerCamelCase  =		[]
              for i in trange(len(A__      )    ,     ascii=A__      ):
                     __lowerCamelCase  =		TreebankWordDetokenizer().detokenize(vars(train_data[i]      )["""text"""]      )
                     __lowerCamelCase  =		discriminator.tokenizer.encode(A__      )
                     __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                     x.append(A__      )
                     y.append(classaidx[vars(train_data[i]      )["""label"""]]      )
              __lowerCamelCase  =		Dataset(A__    ,     A__      )

              __lowerCamelCase  =		[]
              __lowerCamelCase  =		[]
              for i in trange(len(A__      )    ,     ascii=A__      ):
                     __lowerCamelCase  =		TreebankWordDetokenizer().detokenize(vars(test_data[i]      )["""text"""]      )
                     __lowerCamelCase  =		discriminator.tokenizer.encode(A__      )
                     __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                     test_x.append(A__      )
                     test_y.append(classaidx[vars(test_data[i]      )["""label"""]]      )
              __lowerCamelCase  =		Dataset(A__    ,     A__      )

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

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

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

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

                                   if len(A__      ) < max_length_seq:
                                          __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                                   else:
                                          print("""Line {} is longer than maximum length {}""".format(A__    ,     A__      )      )
                                          continue
                                   x.append(A__      )
                                   y.append(d["""label"""]      )
                            except Exception:
                                   print("""Error evaluating / tokenizing line {}, skipping it""".format(A__      )      )
                                   pass

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

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

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

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

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

                                   if len(A__      ) < max_length_seq:
                                          __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )
                                   else:
                                          print("""Line {} is longer than maximum length {}""".format(A__    ,     A__      )      )
                                          continue
                                   x.append(A__      )
                                   y.append(int(np.sum(d["""label"""]      ) > 0      )      )
                            except Exception:
                                   print("""Error evaluating / tokenizing line {}, skipping it""".format(A__      )      )
                                   pass

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

              __lowerCamelCase  =		{
                  """class_size""": len(A__      ),
                  """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."""      )

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

              __lowerCamelCase  =		sorted(A__      )
              __lowerCamelCase  =		{c: i for i, c in enumerate(A__      )}

              __lowerCamelCase  =		Discriminator(
                  class_size=len(A__      )    ,     pretrained_model=A__    ,     cached_mode=A__    ,     device=A__      ).to(A__      )

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

                                   try:
                                          __lowerCamelCase  =		discriminator.tokenizer.encode(A__      )
                                          if len(A__      ) < max_length_seq:
                                                 __lowerCamelCase  =		torch.tensor([50256] + seq    ,     device=A__    ,     dtype=torch.long      )

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

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

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

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

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

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

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

              __lowerCamelCase  =		time.time()

              __lowerCamelCase  =		get_cached_data_loader(A__    ,     A__    ,     A__    ,     shuffle=A__    ,     device=A__      )

              __lowerCamelCase  =		get_cached_data_loader(A__    ,     A__    ,     A__    ,     device=A__      )

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

       else:
              __lowerCamelCase  =		torch.utils.data.DataLoader(
                  dataset=A__    ,     batch_size=A__    ,     shuffle=A__    ,     collate_fn=A__      )
              __lowerCamelCase  =		torch.utils.data.DataLoader(dataset=A__    ,     batch_size=A__    ,     collate_fn=A__      )

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

       __lowerCamelCase  =		optim.Adam(discriminator.parameters()    ,     lr=0.0_001      )

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

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

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

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

              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(A__    ,     epoch + 1      )    ,     )


if __name__ == "__main__":
       UpperCAmelCase_   							=      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_   							=      parser.parse_args()

       train_discriminator(**(vars(args)))

