initial push

added_tokens.json

@@ -0,0 +1,4 @@
+{
+  "<mask>": 49152,
+  "<pad>": 49153
+}

config.json

@@ -0,0 +1,25 @@
+{
+    "_name_or_path": "codesage/codesage-large-v2",
+    "architectures": [
+        "CodeSage"
+    ],
+    "auto_map": {
+        "AutoConfig": "config_codesage.CodeSageConfig",
+        "AutoTokenizer": "tokenization_codesage.CodeSageTokenizer",
+        "AutoModel": "modeling_codesage.CodeSageModel",
+        "AutoModelForMaskedLM": "modeling_codesage.CodeSageForMaskedLM",
+        "AutoModelForSequenceClassification": "modeling_codesage.CodeSageForSequenceClassification"
+    },
+    "activation_function": "gelu_new",
+    "attention_dropout_prob": 0.1,
+    "embedding_dropout_prob": 0.1,
+    "initializer_range": 0.02,
+    "layer_norm_epsilon": 1e-05,
+    "hidden_size": 1024,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "intermediate_size": 8192,
+    "max_position_embeddings": 2048,
+    "residual_dropout_prob": 0.1,
+    "vocab_size": 49154
+}

config_codesage.py

@@ -0,0 +1,52 @@
+#!/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)

modeling_codesage.py

@@ -0,0 +1,426 @@
+#!/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,
+        )

special_tokens_map.json

@@ -0,0 +1,28 @@
+{
+  "additional_special_tokens": [
+    "<|endoftext|>",
+    "<fim_prefix>",
+    "<fim_middle>",
+    "<fim_suffix>",
+    "<fim_pad>",
+    "<filename>",
+    "<gh_stars>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<empty_output>",
+    "<commit_before>",
+    "<commit_msg>",
+    "<commit_after>",
+    "<reponame>"
+  ],
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "mask_token": "<mask>",
+  "pad_token": "<pad>",
+  "unk_token": "<|endoftext|>"
+}

tokenization_codesage.py

@@ -0,0 +1,277 @@
+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 %}"

tokenizer_config.json

@@ -0,0 +1,34 @@
+{
+  "add_prefix_space": false,
+  "additional_special_tokens": [
+    "<|endoftext|>",
+    "<fim_prefix>",
+    "<fim_middle>",
+    "<fim_suffix>",
+    "<fim_pad>",
+    "<filename>",
+    "<gh_stars>",
+    "<issue_start>",
+    "<issue_comment>",
+    "<issue_closed>",
+    "<jupyter_start>",
+    "<jupyter_text>",
+    "<jupyter_code>",
+    "<jupyter_output>",
+    "<empty_output>",
+    "<commit_before>",
+    "<commit_msg>",
+    "<commit_after>",
+    "<reponame>"
+  ],
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "add_eos_token": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152,
+  "tokenizer_class": "CodeSageTokenizer",
+  "auto_map": {
+    "AutoTokenizer": ["tokenization_codesage.CodeSageTokenizer", null]
+  }
+}