remove redundant file

.ipynb_checkpoints/config-checkpoint.json

@@ -1,31 +0,0 @@
-{
-  "_name_or_path": "/mnt/efs/people/dejiaoz/experiments/next_models/codesage_v2_pub/cs-small-clv7-fv1-3e05-4k18k-mlmdobf-para-homo-t003-mrl/combined_bf16_unwrapped.pt",
-  "activation_function": "gelu_new",
-  "architectures": [
-    "CodeSageModel"
-  ],
-  "attention_dropout_prob": 0.1,
-  "auto_map": {
-    "AutoConfig": "config_codesage.CodeSageConfig",
-    "AutoModel": "modeling_codesage.CodeSageModel",
-    "AutoModelForMaskedLM": "modeling_codesage.CodeSageForMaskedLM",
-    "AutoModelForSequenceClassification": "modeling_codesage.CodeSageForSequenceClassification"
-  },
-  "bos_token_id": 100257,
-  "embedding_dropout_prob": 0.1,
-  "eos_token_id": 100257,
-  "hidden_size": 1024,
-  "initializer_range": 0.02,
-  "intermediate_size": 4096,
-  "layer_norm_epsilon": 1e-05,
-  "max_position_embeddings": 2048,
-  "model_type": "codesage",
-  "num_attention_heads": 8,
-  "num_hidden_layers": 6,
-  "pad_token_id": 100317,
-  "position_embedding_type": "absolute",
-  "residual_dropout_prob": 0.1,
-  "torch_dtype": "bfloat16",
-  "transformers_version": "4.26.1",
-  "vocab_size": 49154
-}

.ipynb_checkpoints/config_codesage-checkpoint.py

@@ -1,52 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
-
-from transformers.configuration_utils import PretrainedConfig
-
-CODESAGE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "codesage/codesage-small-v2": "https://huggingface.co/codesage/codesage-small-v2/resolve/main/config.json",
-    "codesage/codesage-base-v2": "https://huggingface.co/codesage/codesage-base-v2/resolve/main/config.json",
-    "codesage/codesage-large-v2": "https://huggingface.co/codesage/codesage-large-v2/resolve/main/config.json",
-}
-
-
-class CodeSageConfig(PretrainedConfig):
-    model_type = "codesage"
-
-    def __init__(
-            self,
-            vocab_size=50257,
-            max_position_embeddings=1024,
-            hidden_size=768,
-            num_hidden_layers=12,
-            num_attention_heads=12,
-            intermediate_size=3072,
-            activation_function="gelu_new",
-            residual_dropout_prob=0.1,
-            embedding_dropout_prob=0.1,
-            attention_dropout_prob=0.1,
-            layer_norm_epsilon=1e-5,
-            initializer_range=0.02,
-            position_embedding_type='absolute',
-            bos_token_id=0,
-            eos_token_id=0,
-            pad_token_id=49153,
-            **kwargs
-    ):
-        self.vocab_size = vocab_size
-        self.max_position_embeddings = max_position_embeddings
-        self.hidden_size = hidden_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.intermediate_size = intermediate_size
-        assert 'gelu' in activation_function
-        self.activation_function = activation_function
-        self.residual_dropout_prob = residual_dropout_prob
-        self.embedding_dropout_prob = embedding_dropout_prob
-        self.attention_dropout_prob = attention_dropout_prob
-        self.layer_norm_epsilon = layer_norm_epsilon
-        self.initializer_range = initializer_range
-        self.position_embedding_type = position_embedding_type
-
-        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

.ipynb_checkpoints/modeling_codesage-checkpoint.py

@@ -1,426 +0,0 @@
-#!/usr/bin/env python
-# coding=utf-8
-# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
-
-import math
-import torch
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss
-from transformers.activations import ACT2FN
-from transformers.modeling_utils import Conv1D, PreTrainedModel
-from transformers.utils import logging
-from .config_codesage import CodeSageConfig
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPooling,
-    MaskedLMOutput,
-    SequenceClassifierOutput
-)
-
-logger = logging.get_logger(__name__)
-
-CODESAGE_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "codesage/codesage-small-v2",
-    "codesage/codesage-base-v2",
-    "codesage/codesage-large-v2",
-    # See all CodeSage models at https://huggingface.co/models?filter=codesage
-]
-
-
-class CodeSageAttention(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = config.hidden_size // self.num_heads
-        if self.head_dim * self.num_heads != config.hidden_size:
-            raise ValueError(
-                f"`hidden_size` must be divisible by num_heads "
-                f"(got `hidden_size`: {config.hidden_size} and `num_heads`: {self.num_heads})."
-            )
-
-        self.c_attn = Conv1D(3 * self.hidden_size, self.hidden_size)
-        self.c_proj = Conv1D(self.hidden_size, self.hidden_size)
-
-        self.attention_dropout = nn.Dropout(config.attention_dropout_prob)
-        self.residual_dropout = nn.Dropout(config.residual_dropout_prob)
-
-    def attn(self, query, key, value, attention_mask=None, head_mask=None):
-        attn_weights = torch.matmul(query, key.transpose(-1, -2))
-        attn_weights = attn_weights / math.sqrt(self.head_dim)
-        if attention_mask is not None:
-            attn_weights = attn_weights + attention_mask
-
-        attn_weights = nn.Softmax(dim=-1)(attn_weights)
-        attn_weights = self.attention_dropout(attn_weights)
-        if head_mask is not None:
-            attn_weights = attn_weights * head_mask
-
-        attn_output = torch.matmul(attn_weights, value)
-        return attn_output, attn_weights
-
-    def split_heads(self, tensor, num_heads, attn_head_size):
-        """
-        Splits hidden_size dim into attn_head_size and num_heads
-        """
-        new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
-        tensor = tensor.view(*new_shape)
-        return tensor.permute(0, 2, 1, 3)  # (batch, head, seq_length, head_features)
-
-    def merge_heads(self, tensor, num_heads, attn_head_size):
-        """
-        Merges attn_head_size dim and num_attn_heads dim into hidden_size
-        """
-        tensor = tensor.permute(0, 2, 1, 3).contiguous()
-        new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
-        return tensor.view(new_shape)
-
-    def forward(
-            self,
-            hidden_states,
-            attention_mask=None,
-            head_mask=None,
-            output_attentions=False,
-    ):
-        query, key, value = self.c_attn(hidden_states).split(self.hidden_size, dim=2)
-        query = self.split_heads(query, self.num_heads, self.head_dim)
-        key = self.split_heads(key, self.num_heads, self.head_dim)
-        value = self.split_heads(value, self.num_heads, self.head_dim)
-
-        attn_output, attn_weights = self.attn(query, key, value, attention_mask, head_mask)
-
-        attn_output = self.merge_heads(attn_output, self.num_heads, self.head_dim)
-        attn_output = self.c_proj(attn_output)
-        attn_output = self.residual_dropout(attn_output)
-
-        outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
-        return outputs  # a, present, (attentions)
-
-
-class CodeSageMLP(nn.Module):
-    def __init__(self, intermediate_size, config):
-        super().__init__()
-
-        self.c_fc = Conv1D(intermediate_size, config.hidden_size)
-        self.act = ACT2FN[config.activation_function]
-        self.c_proj = Conv1D(config.hidden_size, intermediate_size)
-        self.dropout = nn.Dropout(config.residual_dropout_prob)
-
-    def forward(self, hidden_states):
-        hidden_states = self.c_fc(hidden_states)
-        hidden_states = self.act(hidden_states)
-        hidden_states = self.c_proj(hidden_states)
-        hidden_states = self.dropout(hidden_states)
-        return hidden_states
-
-
-class CodeSageBlock(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        hidden_size = config.hidden_size
-        inner_dim = config.intermediate_size if config.intermediate_size is not None else 4 * hidden_size
-        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.attn = CodeSageAttention(config)
-        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
-        self.mlp = CodeSageMLP(inner_dim, config)
-
-    def forward(
-            self,
-            hidden_states,
-            attention_mask=None,
-            head_mask=None,
-            output_attentions=False,
-    ):
-        residual = hidden_states
-        hidden_states = self.ln_1(hidden_states)
-        attn_outputs = self.attn(
-            hidden_states,
-            attention_mask=attention_mask,
-            head_mask=head_mask,
-            output_attentions=output_attentions
-        )
-        attn_output = attn_outputs[0]  # output_attn: a, present, (attentions)
-        outputs = attn_outputs[1:]
-        hidden_states = attn_output + residual
-
-        residual = hidden_states
-        hidden_states = self.ln_2(hidden_states)
-        feed_forward_hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + feed_forward_hidden_states
-
-        outputs = (hidden_states,) + outputs[1:]
-        return outputs  # hidden_states, present, (attentions)
-
-
-class CodeSagePreTrainedModel(PreTrainedModel):
-    config_class = CodeSageConfig
-    base_model_prefix = "transformer"
-
-    def _init_weights(self, module):
-        """Initialize the weights."""
-        if isinstance(module, (nn.Linear, Conv1D)):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-
-class CodeSageModel(CodeSagePreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-
-        self.wte = nn.Embedding(config.vocab_size, config.hidden_size)
-        self.wpe = nn.Embedding(config.max_position_embeddings, config.hidden_size)
-
-        self.drop = nn.Dropout(config.embedding_dropout_prob)
-        self.h = nn.ModuleList([CodeSageBlock(config) for _ in range(config.num_hidden_layers)])
-        self.ln_f = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
-
-        self.init_weights()
-
-    def get_input_embeddings(self):
-        return self.wte
-
-    def set_input_embeddings(self, new_embeddings: torch.Tensor):
-        self.wte = new_embeddings
-
-    def forward(
-            self,
-            input_ids=None,
-            attention_mask=None,
-            position_ids=None,
-            head_mask=None,
-            inputs_embeds=None,
-            output_attentions=None,
-            output_hidden_states=None,
-            return_dict=None
-    ):
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
-        if input_ids is not None:
-            input_shape = input_ids.size()
-        elif inputs_embeds is not None:
-            input_shape = inputs_embeds.size()[:-1]
-        else:
-            raise ValueError("You have to specify either input_ids or inputs_embeds")
-
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-        if position_ids is None:
-            position_ids = torch.arange(input_shape[-1], dtype=torch.long, device=device)
-            position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
-        else:
-            position_ids = position_ids.view(-1, input_shape[-1])
-
-        extended_attention_mask = None
-        if attention_mask is not None:
-            assert attention_mask.dim() == 2
-            extended_attention_mask = attention_mask[:, None, None, :]
-            extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)  # fp16 compatibility
-            extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
-
-        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
-        if inputs_embeds is None:
-            inputs_embeds = self.wte(input_ids)
-
-        position_embeds = self.wpe(position_ids)
-        hidden_states = inputs_embeds + position_embeds
-
-        hidden_states = self.drop(hidden_states)
-        output_shape = input_shape + (hidden_states.size(-1),)
-
-        all_self_attentions = () if output_attentions else None
-        all_hidden_states = () if output_hidden_states else None
-        for i, block in enumerate(self.h):
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            outputs = block(
-                hidden_states,
-                attention_mask=extended_attention_mask,
-                head_mask=head_mask[i],
-                output_attentions=output_attentions,
-            )
-
-            hidden_states = outputs[0]
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (outputs[1],)
-
-        hidden_states = self.ln_f(hidden_states)
-        hidden_states = hidden_states.view(*output_shape)
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        pooled_output = None  # max-pooled output
-        if attention_mask is not None:
-            pooled_output = (hidden_states * attention_mask[:, :, None]).sum(1) / attention_mask.sum(1)[:, None]
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [hidden_states, pooled_output, all_hidden_states, all_self_attentions]
-                if v is not None
-            )
-
-        return BaseModelOutputWithPooling(
-            last_hidden_state=hidden_states,
-            pooler_output=pooled_output,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions
-        )
-
-
-class CodeSageForMaskedLM(CodeSagePreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.transformer = CodeSageModel(config)
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        self.init_weights()
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def forward(
-            self,
-            input_ids=None,
-            attention_mask=None,
-            position_ids=None,
-            head_mask=None,
-            inputs_embeds=None,
-            labels=None,
-            output_attentions=None,
-            output_hidden_states=None,
-            return_dict=None
-    ):
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        transformer_outputs = self.transformer(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict
-        )
-        hidden_states = transformer_outputs[0]
-        lm_logits = self.lm_head(hidden_states)
-
-        masked_lm_loss = None
-        if labels is not None:
-            loss_fct = CrossEntropyLoss()
-            masked_lm_loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
-
-        if not return_dict:
-            output = (lm_logits,) + transformer_outputs[1:]
-            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-
-        return MaskedLMOutput(
-            loss=masked_lm_loss,
-            logits=lm_logits,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-
-class CodeSageForSequenceClassification(CodeSagePreTrainedModel):
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.config = config
-
-        self.transformer = CodeSageModel(config)
-        classifier_dropout = (
-            config.classifier_dropout 
-            if hasattr(config, 'classifier_dropout') and config.classifier_dropout is not None 
-            else config.residual_dropout_prob
-        )
-        self.dropout = nn.Dropout(classifier_dropout)
-        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(
-            self,
-            input_ids=None,
-            attention_mask=None,
-            position_ids=None,
-            head_mask=None,
-            inputs_embeds=None,
-            labels=None,
-            output_attentions=None,
-            output_hidden_states=None,
-            return_dict=None,
-    ):
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-        assert attention_mask is not None, "attention_mask is needed to perform max-pooling"
-
-        outputs = self.transformer(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            head_mask=head_mask,
-            inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        pooled_output = outputs[1]
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-
-        loss = None
-        if labels is not None:
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(logits, labels)
-
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )

.ipynb_checkpoints/tokenization_codesage-checkpoint.py

@@ -1,277 +0,0 @@
-import json
-import os
-from functools import lru_cache
-from typing import List, Optional, Tuple
-
-import regex as re
-
-from transformers import AddedToken, PreTrainedTokenizer
-import logging
-
-
-logger = logging.getLogger(__name__)
-
-VOCAB_FILES_NAMES = {
-    "vocab_file": "vocab.json",
-    "merges_file": "merges.txt",
-}
-
-# Taken from
-# https://github.com/huggingface/transformers/blob/8aca43bdb3cb9a5020f6d57589d85679dc873b1c/src/transformers/models/gpt2/tokenization_gpt2.py#L62-L84
-@lru_cache()
-def bytes_to_unicode():
-    """
-    Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
-    characters the bpe code barfs on.
-
-    The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
-    if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for
-    decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup
-    tables between utf-8 bytes and unicode strings.
-    """
-    bs = (
-        list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
-    )
-    cs = bs[:]
-    n = 0
-    for b in range(2**8):
-        if b not in bs:
-            bs.append(b)
-            cs.append(2**8 + n)
-            n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
-
-
-def get_pairs(word):
-    """
-    Return set of symbol pairs in a word.
-
-    Word is represented as tuple of symbols (symbols being variable-length strings).
-    """
-    pairs = set()
-    prev_char = word[0]
-    for char in word[1:]:
-        pairs.add((prev_char, char))
-        prev_char = char
-    return pairs
-
-
-class CodeSageTokenizer(PreTrainedTokenizer):
-    """A thin wrapper of the starcoder tokenizer.
-    See HuggingFace for further documentation on general tokenizer methods.
-    """
-
-    vocab_files_names = VOCAB_FILES_NAMES
-    model_input_names = ["input_ids", "attention_mask"]
-
-    def __init__(
-        self,
-        vocab_file,
-        merges_file,
-        errors="replace",
-        unk_token="<|endoftext|>",
-        bos_token="<|endoftext|>",
-        eos_token="<|endoftext|>",
-        pad_token=None,
-        add_prefix_space=False,
-        add_bos_token=False,
-        add_eos_token=True,
-        **kwargs,
-    ):
-        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
-        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
-        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
-        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
-
-        self.add_bos_token = add_bos_token
-        self.add_eos_token = add_eos_token
-
-        with open(vocab_file, encoding="utf-8") as vocab_handle:
-            self.encoder = json.load(vocab_handle)
-        self.decoder = {v: k for k, v in self.encoder.items()}
-        self.errors = errors  # how to handle errors in decoding
-        self.byte_encoder = bytes_to_unicode()
-        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
-        with open(merges_file, encoding="utf-8") as merges_handle:
-            bpe_merges = merges_handle.read().split("\n")[1:-1]
-        bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
-        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
-        self.cache = {}
-        self.add_prefix_space = add_prefix_space
-
-        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
-        self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")
-
-        super().__init__(
-            errors=errors,
-            unk_token=unk_token,
-            bos_token=bos_token,
-            eos_token=eos_token,
-            pad_token=pad_token,
-            add_prefix_space=add_prefix_space,
-            add_bos_token=add_bos_token,
-            add_eos_token=add_eos_token,
-            **kwargs,
-        )
-
-    @property
-    def vocab_size(self):
-        return len(self.encoder)
-
-    def get_vocab(self):
-        return dict(self.encoder, **self.added_tokens_encoder)
-
-    def bpe(self, token):
-        if token in self.cache:
-            return self.cache[token]
-        word = tuple(token)
-        pairs = get_pairs(word)
-
-        if not pairs:
-            return token
-
-        while True:
-            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
-            if bigram not in self.bpe_ranks:
-                break
-            first, second = bigram
-            new_word = []
-            i = 0
-            while i < len(word):
-                try:
-                    j = word.index(first, i)
-                except ValueError:
-                    new_word.extend(word[i:])
-                    break
-                else:
-                    new_word.extend(word[i:j])
-                    i = j
-
-                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
-                    new_word.append(first + second)
-                    i += 2
-                else:
-                    new_word.append(word[i])
-                    i += 1
-            new_word = tuple(new_word)
-            word = new_word
-            if len(word) == 1:
-                break
-            else:
-                pairs = get_pairs(word)
-        word = " ".join(word)
-        self.cache[token] = word
-        return word
-
-    def build_inputs_with_special_tokens(
-            self,
-            token_ids_0: List[int],
-            token_ids_1: Optional[List[int]] = None) -> List[int]:
-        bos_token_id = [self.bos_token_id] if self.add_bos_token else []
-        eos_token_id = [self.eos_token_id] if self.add_eos_token else []
-
-        output = bos_token_id + token_ids_0 + eos_token_id
-
-        if token_ids_1 is not None:
-            output = output + bos_token_id + token_ids_1 + eos_token_id
-
-        return output
-
-    def get_special_tokens_mask(
-        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
-    ) -> List[int]:
-        """
-        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
-        special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
-
-        Args:
-            token_ids_0 (`List[int]`):
-                List of IDs.
-            token_ids_1 (`List[int]`, *optional*):
-                Optional second list of IDs for sequence pairs.
-            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
-                Whether or not the token list is already formatted with special tokens for the model.
-
-        Returns:
-            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
-        """
-        if already_has_special_tokens:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
-            )
-
-        if not self.add_bos_token:
-            return super().get_special_tokens_mask(
-                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=False
-            )
-
-        if token_ids_1 is None:
-            return [1] + ([0] * len(token_ids_0))
-        return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
-
-    def _tokenize(self, text):
-        """Tokenize a string."""
-        bpe_tokens = []
-        for token in re.findall(self.pat, text):
-            token = "".join(
-                self.byte_encoder[b] for b in token.encode("utf-8")
-            )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
-            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
-        return bpe_tokens
-
-    def _convert_token_to_id(self, token):
-        """Converts a token (str) in an id using the vocab."""
-        return self.encoder.get(token, self.encoder.get(self.unk_token))
-
-    def _convert_id_to_token(self, index):
-        """Converts an index (integer) in a token (str) using the vocab."""
-        return self.decoder.get(index)
-
-    def convert_tokens_to_string(self, tokens):
-        """Converts a sequence of tokens (string) in a single string."""
-        text = "".join(tokens)
-        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
-        return text
-
-    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
-        if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
-            return
-        vocab_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
-        )
-        merge_file = os.path.join(
-            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
-        )
-
-        with open(vocab_file, "w", encoding="utf-8") as f:
-            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
-
-        index = 0
-        with open(merge_file, "w", encoding="utf-8") as writer:
-            writer.write("#version: 0.2\n")
-            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
-                if index != token_index:
-                    logger.warning(
-                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
-                        " Please check that the tokenizer is not corrupted!"
-                    )
-                    index = token_index
-                writer.write(" ".join(bpe_tokens) + "\n")
-                index += 1
-
-        return vocab_file, merge_file
-
-    def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
-        add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
-        if is_split_into_words or add_prefix_space:
-            text = " " + text
-        return (text, kwargs)
-
-    @property
-    def default_chat_template(self):
-        """
-        A simple chat template that ignores role information and just concatenates messages with EOS tokens.
-        """
-        return "{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}"