# -*- coding: utf-8 -*-
"""DeBerta.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1rNHnLatGzTe1bBFgvflqzMkMIszfl1WC
"""

from google.colab import drive
drive.mount('/content/drive')

!pip install pytorch-transformers
!pip install deberta

# Commented out IPython magic to ensure Python compatibility.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %matplotlib inline

df = pd.read_csv('/content/drive/My Drive/Amazon_ReviewData/Reviews.csv')
df.head()

df = df.loc[:, ['Text', 'Score']]
df.head()
def get_sentiment(value):
    if value > 3:
        return 1
    else:
        return 0
df['Sentiment'] = df['Score'].apply(get_sentiment)
df.head()

df['Sentiment'] = [1 if sentiment == 'Positive' else 0 
                            for sentiment in df['Sentiment'].values]
df.head()

train_df = df.iloc[:8000]
val_df = df.iloc[8000:]

train_df.shape, val_df.shape

!pip install pytorch-transformers
!pip install deberta

import csv
import os
import random
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                              TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from pytorch_transformers import BertConfig, BertForSequenceClassification, BertTokenizer
from DeBERTa import deberta
from pytorch_transformers import AdamW, WarmupLinearSchedule
from tqdm import tqdm, trange, tqdm_notebook
from sklearn.metrics import matthews_corrcoef, f1_score

class InputExample(torch.nn.Module):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text_a, text_b=None, label=None):
        """Constructs a InputExample.
        Args:
            guid: Unique id for the example.
            text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
            text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
            label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        super().__init__()

        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label

        self.bert = deberta.DeBERTa(pre_trained='base')
        self.bert.apply_state() # Apply the pre-trained model of DeBERTa at the end of the constructor

class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, label_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id

class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r", encoding="utf-8-sig") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                if sys.version_info[0] == 2:
                    line = list(unicode(cell, 'utf-8') for cell in line)
                lines.append(line)
            return lines
          
class AmazonProcessor(DataProcessor):
    """Processor for the Amazon Reviews data set."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.csv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.csv")), "dev")

    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test.csv")), "test")

    def get_labels(self):
        """See base class."""
        return [0, 1]

    #Hack to be compatible with the existing code in transformers library
    def _read_tsv(self, file_path):
        return pd.read_csv(file_path).values.tolist()

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
               continue
            guid = "%s-%s" % (set_type, i)
            text_a = str(line[0])
           # text_b = None
            label = line[1]
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
        return examples

def set_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)

def simple_accuracy(preds, labels):
  return (preds == labels).mean()
  
def acc_and_f1(preds, labels):
  acc = simple_accuracy(preds, labels)
  f1 = f1_score(y_true=labels, y_pred=preds)
  return {
      "acc": acc,
      "f1": f1,
      "acc_and_f1": (acc + f1) / 2,
  }


def f1_macro(preds, labels):
  return f1_score(y_true=labels, y_pred=preds, average='macro')
  
def compute_metrics(task_name, preds, labels):
  assert len(preds) == len(labels)
  if task_name == "amazon":
    return {"acc": simple_accuracy(preds, labels)}
  else:
    raise KeyError(task_name)

def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()

def convert_examples_to_features(examples, label_list, max_seq_length,
                                 tokenizer, output_mode,
                                 cls_token_at_end=False, pad_on_left=False,
                                 cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
                                 sequence_a_segment_id=0, sequence_b_segment_id=1,
                                 cls_token_segment_id=1, pad_token_segment_id=0,
                                 mask_padding_with_zero=True):
    """ Loads a data file into a list of `InputBatch`s
        `cls_token_at_end` define the location of the CLS token:
            - False (Default, DEBERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
            - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
        `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
    """

    label_map = {label : i for i, label in enumerate(label_list)}

    features = []
    for (ex_index, example) in enumerate(examples):

        tokens_a = tokenizer.tokenize(example.text_a)

        tokens_b = None
        if example.text_b:
            tokens_b = tokenizer.tokenize(example.text_b)
            # Modifies `tokens_a` and `tokens_b` in place so that the total
            # length is less than the specified length.
            # Account for [CLS], [SEP], [SEP] with "- 3"
            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
        else:
            # Account for [CLS] and [SEP] with "- 2"
            if len(tokens_a) > max_seq_length - 2:
                tokens_a = tokens_a[:(max_seq_length - 2)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids:   0   0   0   0  0     0   0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens = tokens_a + [sep_token]
        segment_ids = [sequence_a_segment_id] * len(tokens)

        if tokens_b:
            tokens += tokens_b + [sep_token]
            segment_ids += [sequence_b_segment_id] * (len(tokens_b) + 1)

        if cls_token_at_end:
            tokens = tokens + [cls_token]
            segment_ids = segment_ids + [cls_token_segment_id]
        else:
            tokens = [cls_token] + tokens
            segment_ids = [cls_token_segment_id] + segment_ids

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

        # Zero-pad up to the sequence length.
        padding_length = max_seq_length - len(input_ids)
        if pad_on_left:
            input_ids = ([pad_token] * padding_length) + input_ids
            input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
            segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
        else:
            input_ids = input_ids + ([pad_token] * padding_length)
            input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
            segment_ids = segment_ids + ([pad_token_segment_id] * padding_length)

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        if output_mode == "classification":
            label_id = label_map[example.label]
        elif output_mode == "regression":
            label_id = float(example.label)
        else:
            raise KeyError(output_mode)

        features.append(
                InputFeatures(input_ids=input_ids,
                              input_mask=input_mask,
                              segment_ids=segment_ids,
                              label_id=label_id))
    return features

processor = AmazonProcessor()
label_list = processor.get_labels()
num_labels = len(label_list)

config = BertConfig.from_pretrained('bert-base-uncased', num_labels=num_labels)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')


# -*- coding: utf-8 -*-
"""DeBerta.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1rNHnLatGzTe1bBFgvflqzMkMIszfl1WC
"""

from google.colab import drive
drive.mount('/content/drive')

!pip install pytorch-transformers
!pip install deberta

# Commented out IPython magic to ensure Python compatibility.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# %matplotlib inline

df = pd.read_csv('/content/drive/My Drive/Amazon_ReviewData/Reviews.csv')
df.head()

df = df.loc[:, ['Text', 'Score']]
df.head()
def get_sentiment(value):
    if value > 3:
        return 1
    else:
        return 0
df['Sentiment'] = df['Score'].apply(get_sentiment)
df.head()

df['Sentiment'] = [1 if sentiment == 'Positive' else 0 
                            for sentiment in df['Sentiment'].values]
df.head()

train_df = df.iloc[:8000]
val_df = df.iloc[8000:]

train_df.shape, val_df.shape

!pip install pytorch-transformers
!pip install deberta

import csv
import os
import random
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                              TensorDataset)
from torch.utils.data.distributed import DistributedSampler
from pytorch_transformers import BertConfig, BertForSequenceClassification, BertTokenizer
from DeBERTa import deberta
from pytorch_transformers import AdamW, WarmupLinearSchedule
from tqdm import tqdm, trange, tqdm_notebook
from sklearn.metrics import matthews_corrcoef, f1_score

class InputExample(torch.nn.Module):
    """A single training/test example for simple sequence classification."""

    def __init__(self, guid, text_a, text_b=None, label=None):
        """Constructs a InputExample.
        Args:
            guid: Unique id for the example.
            text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
            text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
            label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        super().__init__()

        self.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label

        self.bert = deberta.DeBERTa(pre_trained='base')
        self.bert.apply_state() # Apply the pre-trained model of DeBERTa at the end of the constructor

class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, label_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id

class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r", encoding="utf-8-sig") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            for line in reader:
                if sys.version_info[0] == 2:
                    line = list(unicode(cell, 'utf-8') for cell in line)
                lines.append(line)
            return lines
          
class AmazonProcessor(DataProcessor):
    """Processor for the Amazon Reviews data set."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.csv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "dev.csv")), "dev")

    def get_test_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "test.csv")), "test")

    def get_labels(self):
        """See base class."""
        return [0, 1]

    #Hack to be compatible with the existing code in transformers library
    def _read_tsv(self, file_path):
        return pd.read_csv(file_path).values.tolist()

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
               continue
            guid = "%s-%s" % (set_type, i)
            text_a = str(line[0])
           # text_b = None
            label = line[1]
            examples.append(
                InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
        return examples

def set_seed(seed):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)

def simple_accuracy(preds, labels):
  return (preds == labels).mean()
  
def acc_and_f1(preds, labels):
  acc = simple_accuracy(preds, labels)
  f1 = f1_score(y_true=labels, y_pred=preds)
  return {
      "acc": acc,
      "f1": f1,
      "acc_and_f1": (acc + f1) / 2,
  }


def f1_macro(preds, labels):
  return f1_score(y_true=labels, y_pred=preds, average='macro')
  
def compute_metrics(task_name, preds, labels):
  assert len(preds) == len(labels)
  if task_name == "amazon":
    return {"acc": simple_accuracy(preds, labels)}
  else:
    raise KeyError(task_name)

def _truncate_seq_pair(tokens_a, tokens_b, max_length):
    """Truncates a sequence pair in place to the maximum length."""

    # This is a simple heuristic which will always truncate the longer sequence
    # one token at a time. This makes more sense than truncating an equal percent
    # of tokens from each, since if one sequence is very short then each token
    # that's truncated likely contains more information than a longer sequence.
    while True:
        total_length = len(tokens_a) + len(tokens_b)
        if total_length <= max_length:
            break
        if len(tokens_a) > len(tokens_b):
            tokens_a.pop()
        else:
            tokens_b.pop()

def convert_examples_to_features(examples, label_list, max_seq_length,
                                 tokenizer, output_mode,
                                 cls_token_at_end=False, pad_on_left=False,
                                 cls_token='[CLS]', sep_token='[SEP]', pad_token=0,
                                 sequence_a_segment_id=0, sequence_b_segment_id=1,
                                 cls_token_segment_id=1, pad_token_segment_id=0,
                                 mask_padding_with_zero=True):
    """ Loads a data file into a list of `InputBatch`s
        `cls_token_at_end` define the location of the CLS token:
            - False (Default, DEBERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]
            - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]
        `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)
    """

    label_map = {label : i for i, label in enumerate(label_list)}

    features = []
    for (ex_index, example) in enumerate(examples):

        tokens_a = tokenizer.tokenize(example.text_a)

        tokens_b = None
        if example.text_b:
            tokens_b = tokenizer.tokenize(example.text_b)
            # Modifies `tokens_a` and `tokens_b` in place so that the total
            # length is less than the specified length.
            # Account for [CLS], [SEP], [SEP] with "- 3"
            _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
        else:
            # Account for [CLS] and [SEP] with "- 2"
            if len(tokens_a) > max_seq_length - 2:
                tokens_a = tokens_a[:(max_seq_length - 2)]

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids:   0   0   0   0  0     0   0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.
        tokens = tokens_a + [sep_token]
        segment_ids = [sequence_a_segment_id] * len(tokens)

        if tokens_b:
            tokens += tokens_b + [sep_token]
            segment_ids += [sequence_b_segment_id] * (len(tokens_b) + 1)

        if cls_token_at_end:
            tokens = tokens + [cls_token]
            segment_ids = segment_ids + [cls_token_segment_id]
        else:
            tokens = [cls_token] + tokens
            segment_ids = [cls_token_segment_id] + segment_ids

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)

        # Zero-pad up to the sequence length.
        padding_length = max_seq_length - len(input_ids)
        if pad_on_left:
            input_ids = ([pad_token] * padding_length) + input_ids
            input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
            segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids
        else:
            input_ids = input_ids + ([pad_token] * padding_length)
            input_mask = input_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
            segment_ids = segment_ids + ([pad_token_segment_id] * padding_length)

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        if output_mode == "classification":
            label_id = label_map[example.label]
        elif output_mode == "regression":
            label_id = float(example.label)
        else:
            raise KeyError(output_mode)

        features.append(
                InputFeatures(input_ids=input_ids,
                              input_mask=input_mask,
                              segment_ids=segment_ids,
                              label_id=label_id))
    return features

processor = AmazonProcessor()
label_list = processor.get_labels()
num_labels = len(label_list)

config = BertConfig.from_pretrained('bert-base-uncased', num_labels=num_labels)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')


# -*- coding: utf-8 -*- DeBerta.ipynb  Automatically generated by Colaboratory.  Original file is located at     https://colab.research.google.com/drive/1rNHnLatGzTe1bBFgvflqzMkMIszfl1WC # Commented out IPython magic to ensure Python compatibility. # %matplotlib inline A single training/test example for simple sequence classification. Constructs a InputExample.         Args:             guid: Unique id for the example.             text_a: string. The untokenized text of the first sequence. For single             sequence tasks, only this sequence must be specified.             text_b: (Optional) string. The untokenized text of the second sequence.             Only must be specified for sequence pair tasks.             label: (Optional) string. The label of the example. This should be             specified for train and dev examples, but not for test examples. # Apply the pre-trained model of DeBERTa at the end of the constructor A single set of features of data. Base class for data converters for sequence classification data sets. Gets a collection of `InputExample`s for the train set. Gets a collection of `InputExample`s for the dev set. Gets the list of labels for this data set. Reads a tab separated value file. Processor for the Amazon Reviews data set. See base class. See base class. See base class. See base class. #Hack to be compatible with the existing code in transformers library Creates examples for the training and dev sets. # text_b = None Truncates a sequence pair in place to the maximum length. # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes more sense than truncating an equal percent # of tokens from each, since if one sequence is very short then each token # that's truncated likely contains more information than a longer sequence. Loads a data file into a list of `InputBatch`s         `cls_token_at_end` define the location of the CLS token:             - False (Default, DEBERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]             - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]         `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet) # Modifies `tokens_a` and `tokens_b` in place so that the total # length is less than the specified length. # Account for [CLS], [SEP], [SEP] with "- 3" # Account for [CLS] and [SEP] with "- 2" # The convention in BERT is: # (a) For sequence pairs: #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP] #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1 # (b) For single sequences: #  tokens:   [CLS] the dog is hairy . [SEP] #  type_ids:   0   0   0   0  0     0   0 # # Where "type_ids" are used to indicate whether this is the first # sequence or the second sequence. The embedding vectors for `type=0` and # `type=1` were learned during pre-training and are added to the wordpiece # embedding vector (and position vector). This is not *strictly* necessary # since the [SEP] token unambiguously separates the sequences, but it makes # it easier for the model to learn the concept of sequences. # # For classification tasks, the first vector (corresponding to [CLS]) is # used as as the "sentence vector". Note that this only makes sense because # the entire model is fine-tuned. # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. # Zero-pad up to the sequence length.