{"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_io","uri":"program://CodeT/module/DIVERSE.code.src.utils_io#L1-L7","kind":"module","name":"DIVERSE.code.src.utils_io","path":"DIVERSE/code/src/utils_io.py","language":"python","start_line":1,"end_line":7,"context_start_line":1,"context_end_line":7,"code":"import os\n\ndef get_file(path):\n if os.path.isdir(path):\n return os.path.join(path, os.listdir(path)[0])\n else:\n return path","source_hash":"8f763f060cbc05ee517cdd35fbd7b2f0c8e34d2dd0149904aba9864a028e5146","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_io.get_file","uri":"program://CodeT/function/DIVERSE.code.src.utils_io.get_file#L3-L7","kind":"function","name":"get_file","path":"DIVERSE/code/src/utils_io.py","language":"python","start_line":3,"end_line":7,"context_start_line":1,"context_end_line":7,"code":"import os\n\ndef get_file(path):\n if os.path.isdir(path):\n return os.path.join(path, os.listdir(path)[0])\n else:\n return path","source_hash":"8f763f060cbc05ee517cdd35fbd7b2f0c8e34d2dd0149904aba9864a028e5146","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model","uri":"program://CodeT/module/DIVERSE.code.src.deberta_model#L1-L1549","kind":"module","name":"DIVERSE.code.src.deberta_model","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1,"end_line":1549,"context_start_line":1,"context_end_line":1549,"code":"# coding=utf-8\n# Copyright 2020 Microsoft and the Hugging Face Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" PyTorch DeBERTa-v2 model. \"\"\"\nimport pdb\nimport math\nfrom collections.abc import Sequence\n\nimport numpy as np\nimport torch\nfrom torch import _softmax_backward_data, nn\nfrom torch.nn import CrossEntropyLoss, LayerNorm\n\nfrom transformers.activations import ACT2FN\nfrom transformers.file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward\nfrom transformers.modeling_outputs import (\n BaseModelOutput,\n MaskedLMOutput,\n QuestionAnsweringModelOutput,\n SequenceClassifierOutput,\n TokenClassifierOutput,\n)\nfrom transformers.modeling_utils import PreTrainedModel\nfrom transformers.utils import logging\nfrom transformers.models.deberta_v2.configuration_deberta_v2 import DebertaV2Config\n\n\nlogger = logging.get_logger(__name__)\n\n_CONFIG_FOR_DOC = \"DebertaV2Config\"\n_TOKENIZER_FOR_DOC = \"DebertaV2Tokenizer\"\n_CHECKPOINT_FOR_DOC = \"microsoft/deberta-v2-xlarge\"\n\nDEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = [\n \"microsoft/deberta-v2-xlarge\",\n \"microsoft/deberta-v2-xxlarge\",\n \"microsoft/deberta-v2-xlarge-mnli\",\n \"microsoft/deberta-v2-xxlarge-mnli\",\n]\n\n\n# Copied from transformers.models.deberta.modeling_deberta.ContextPooler\nclass ContextPooler(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size)\n self.dropout = StableDropout(config.pooler_dropout)\n self.config = config\n\n def forward(self, hidden_states):\n # We \"pool\" the model by simply taking the hidden state corresponding\n # to the first token.\n\n context_token = hidden_states[:, 0]\n context_token = self.dropout(context_token)\n pooled_output = self.dense(context_token)\n pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output)\n return pooled_output\n\n @property\n def output_dim(self):\n return self.config.hidden_size\n\n\n# Copied from transformers.models.deberta.modeling_deberta.XSoftmax with deberta->deberta_v2\nclass XSoftmax(torch.autograd.Function):\n \"\"\"\n Masked Softmax which is optimized for saving memory\n\n Args:\n input (:obj:`torch.tensor`): The input tensor that will apply softmax.\n mask (:obj:`torch.IntTensor`): The mask matrix where 0 indicate that element will be ignored in the softmax calculation.\n dim (int): The dimension that will apply softmax\n\n Example::\n\n >>> import torch\n >>> from transformers.models.deberta_v2.modeling_deberta_v2 import XSoftmax\n\n >>> # Make a tensor\n >>> x = torch.randn([4,20,100])\n\n >>> # Create a mask\n >>> mask = (x>0).int()\n\n >>> y = XSoftmax.apply(x, mask, dim=-1)\n \"\"\"\n\n @staticmethod\n def forward(self, input, mask, dim):\n self.dim = dim\n rmask = ~(mask.bool())\n\n output = input.masked_fill(rmask, float(\"-inf\"))\n output = torch.softmax(output, self.dim)\n output.masked_fill_(rmask, 0)\n self.save_for_backward(output)\n return output\n\n @staticmethod\n def backward(self, grad_output):\n (output,) = self.saved_tensors\n inputGrad = _softmax_backward_data(grad_output, output, self.dim, output)\n return inputGrad, None, None\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DropoutContext\nclass DropoutContext(object):\n def __init__(self):\n self.dropout = 0\n self.mask = None\n self.scale = 1\n self.reuse_mask = True\n\n\n# Copied from transformers.models.deberta.modeling_deberta.get_mask\ndef get_mask(input, local_context):\n if not isinstance(local_context, DropoutContext):\n dropout = local_context\n mask = None\n else:\n dropout = local_context.dropout\n dropout *= local_context.scale\n mask = local_context.mask if local_context.reuse_mask else None\n\n if dropout > 0 and mask is None:\n mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).bool()\n\n if isinstance(local_context, DropoutContext):\n if local_context.mask is None:\n local_context.mask = mask\n\n return mask, dropout\n\n\n# Copied from transformers.models.deberta.modeling_deberta.XDropout\nclass XDropout(torch.autograd.Function):\n \"\"\"Optimized dropout function to save computation and memory by using mask operation instead of multiplication.\"\"\"\n\n @staticmethod\n def forward(ctx, input, local_ctx):\n mask, dropout = get_mask(input, local_ctx)\n ctx.scale = 1.0 / (1 - dropout)\n if dropout > 0:\n ctx.save_for_backward(mask)\n return input.masked_fill(mask, 0) * ctx.scale\n else:\n return input\n\n @staticmethod\n def backward(ctx, grad_output):\n if ctx.scale > 1:\n (mask,) = ctx.saved_tensors\n return grad_output.masked_fill(mask, 0) * ctx.scale, None\n else:\n return grad_output, None\n\n\n# Copied from transformers.models.deberta.modeling_deberta.StableDropout\nclass StableDropout(nn.Module):\n \"\"\"\n Optimized dropout module for stabilizing the training\n\n Args:\n drop_prob (float): the dropout probabilities\n \"\"\"\n\n def __init__(self, drop_prob):\n super().__init__()\n self.drop_prob = drop_prob\n self.count = 0\n self.context_stack = None\n\n def forward(self, x):\n \"\"\"\n Call the module\n\n Args:\n x (:obj:`torch.tensor`): The input tensor to apply dropout\n \"\"\"\n if self.training and self.drop_prob > 0:\n return XDropout.apply(x, self.get_context())\n return x\n\n def clear_context(self):\n self.count = 0\n self.context_stack = None\n\n def init_context(self, reuse_mask=True, scale=1):\n if self.context_stack is None:\n self.context_stack = []\n self.count = 0\n for c in self.context_stack:\n c.reuse_mask = reuse_mask\n c.scale = scale\n\n def get_context(self):\n if self.context_stack is not None:\n if self.count >= len(self.context_stack):\n self.context_stack.append(DropoutContext())\n ctx = self.context_stack[self.count]\n ctx.dropout = self.drop_prob\n self.count += 1\n return ctx\n else:\n return self.drop_prob\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm\nclass DebertaV2SelfOutput(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)\n self.dropout = StableDropout(config.hidden_dropout_prob)\n\n def forward(self, hidden_states, input_tensor):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.LayerNorm(hidden_states + input_tensor)\n return hidden_states\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2\nclass DebertaV2Attention(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.self = DisentangledSelfAttention(config)\n self.output = DebertaV2SelfOutput(config)\n self.config = config\n\n def forward(\n self,\n hidden_states,\n attention_mask,\n return_att=False,\n query_states=None,\n relative_pos=None,\n rel_embeddings=None,\n ):\n self_output = self.self(\n hidden_states,\n attention_mask,\n return_att,\n query_states=query_states,\n relative_pos=relative_pos,\n rel_embeddings=rel_embeddings,\n )\n if return_att:\n self_output, att_matrix = self_output\n if query_states is None:\n query_states = hidden_states\n attention_output = self.output(self_output, query_states)\n\n if return_att:\n return (attention_output, att_matrix)\n else:\n return attention_output\n\n\n# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->DebertaV2\nclass DebertaV2Intermediate(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.intermediate_size)\n if isinstance(config.hidden_act, str):\n self.intermediate_act_fn = ACT2FN[config.hidden_act]\n else:\n self.intermediate_act_fn = config.hidden_act\n\n def forward(self, hidden_states):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.intermediate_act_fn(hidden_states)\n return hidden_states\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaOutput with DebertaLayerNorm->LayerNorm\nclass DebertaV2Output(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.intermediate_size, config.hidden_size)\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)\n self.dropout = StableDropout(config.hidden_dropout_prob)\n self.config = config\n\n def forward(self, hidden_states, input_tensor):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.LayerNorm(hidden_states + input_tensor)\n return hidden_states\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaLayer with Deberta->DebertaV2\nclass DebertaV2Layer(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.attention = DebertaV2Attention(config)\n self.intermediate = DebertaV2Intermediate(config)\n self.output = DebertaV2Output(config)\n\n def forward(\n self,\n hidden_states,\n attention_mask,\n return_att=False,\n query_states=None,\n relative_pos=None,\n rel_embeddings=None,\n ):\n attention_output = self.attention(\n hidden_states,\n attention_mask,\n return_att=return_att,\n query_states=query_states,\n relative_pos=relative_pos,\n rel_embeddings=rel_embeddings,\n )\n if return_att:\n attention_output, att_matrix = attention_output\n intermediate_output = self.intermediate(attention_output)\n layer_output = self.output(intermediate_output, attention_output)\n if return_att:\n return (layer_output, att_matrix)\n else:\n return layer_output\n\n\nclass ConvLayer(nn.Module):\n def __init__(self, config):\n super().__init__()\n kernel_size = getattr(config, \"conv_kernel_size\", 3)\n groups = getattr(config, \"conv_groups\", 1)\n self.conv_act = getattr(config, \"conv_act\", \"tanh\")\n self.conv = nn.Conv1d(\n config.hidden_size, config.hidden_size, kernel_size, padding=(kernel_size - 1) // 2, groups=groups\n )\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)\n self.dropout = StableDropout(config.hidden_dropout_prob)\n self.config = config\n\n def forward(self, hidden_states, residual_states, input_mask):\n out = self.conv(hidden_states.permute(0, 2, 1).contiguous()).permute(0, 2, 1).contiguous()\n rmask = (1 - input_mask).bool()\n out.masked_fill_(rmask.unsqueeze(-1).expand(out.size()), 0)\n out = ACT2FN[self.conv_act](self.dropout(out))\n\n layer_norm_input = residual_states + out\n output = self.LayerNorm(layer_norm_input).to(layer_norm_input)\n\n if input_mask is None:\n output_states = output\n else:\n if input_mask.dim() != layer_norm_input.dim():\n if input_mask.dim() == 4:\n input_mask = input_mask.squeeze(1).squeeze(1)\n input_mask = input_mask.unsqueeze(2)\n\n input_mask = input_mask.to(output.dtype)\n output_states = output * input_mask\n\n return output_states\n\n\nclass DebertaV2Encoder(nn.Module):\n \"\"\"Modified BertEncoder with relative position bias support\"\"\"\n\n def __init__(self, config):\n super().__init__()\n\n self.layer = nn.ModuleList([DebertaV2Layer(config) for _ in range(config.num_hidden_layers)])\n self.relative_attention = getattr(config, \"relative_attention\", False)\n\n if self.relative_attention:\n self.max_relative_positions = getattr(config, \"max_relative_positions\", -1)\n if self.max_relative_positions < 1:\n self.max_relative_positions = config.max_position_embeddings\n\n self.position_buckets = getattr(config, \"position_buckets\", -1)\n pos_ebd_size = self.max_relative_positions * 2\n\n if self.position_buckets > 0:\n pos_ebd_size = self.position_buckets * 2\n\n self.rel_embeddings = nn.Embedding(pos_ebd_size, config.hidden_size)\n\n self.norm_rel_ebd = [x.strip() for x in getattr(config, \"norm_rel_ebd\", \"none\").lower().split(\"|\")]\n\n if \"layer_norm\" in self.norm_rel_ebd:\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True)\n\n self.conv = ConvLayer(config) if getattr(config, \"conv_kernel_size\", 0) > 0 else None\n\n def get_rel_embedding(self):\n rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None\n if rel_embeddings is not None and (\"layer_norm\" in self.norm_rel_ebd):\n rel_embeddings = self.LayerNorm(rel_embeddings)\n return rel_embeddings\n\n def get_attention_mask(self, attention_mask):\n if attention_mask.dim() <= 2:\n extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)\n attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1)\n attention_mask = attention_mask.byte()\n elif attention_mask.dim() == 3:\n attention_mask = attention_mask.unsqueeze(1)\n\n return attention_mask\n\n def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None):\n if self.relative_attention and relative_pos is None:\n q = query_states.size(-2) if query_states is not None else hidden_states.size(-2)\n relative_pos = build_relative_position(\n q, hidden_states.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions\n )\n return relative_pos\n\n def forward(\n self,\n hidden_states,\n attention_mask,\n output_hidden_states=True,\n output_attentions=False,\n query_states=None,\n relative_pos=None,\n return_dict=True,\n ):\n if attention_mask.dim() <= 2:\n input_mask = attention_mask\n else:\n input_mask = (attention_mask.sum(-2) > 0).byte()\n attention_mask = self.get_attention_mask(attention_mask)\n relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos)\n\n all_hidden_states = () if output_hidden_states else None\n all_attentions = () if output_attentions else None\n\n if isinstance(hidden_states, Sequence):\n next_kv = hidden_states[0]\n else:\n next_kv = hidden_states\n rel_embeddings = self.get_rel_embedding()\n output_states = next_kv\n for i, layer_module in enumerate(self.layer):\n\n if output_hidden_states:\n all_hidden_states = all_hidden_states + (output_states,)\n\n output_states = layer_module(\n next_kv,\n attention_mask,\n output_attentions,\n query_states=query_states,\n relative_pos=relative_pos,\n rel_embeddings=rel_embeddings,\n )\n if output_attentions:\n output_states, att_m = output_states\n\n if i == 0 and self.conv is not None:\n output_states = self.conv(hidden_states, output_states, input_mask)\n\n if query_states is not None:\n query_states = output_states\n if isinstance(hidden_states, Sequence):\n next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None\n else:\n next_kv = output_states\n\n if output_attentions:\n all_attentions = all_attentions + (att_m,)\n\n if output_hidden_states:\n all_hidden_states = all_hidden_states + (output_states,)\n\n if not return_dict:\n return tuple(v for v in [output_states, all_hidden_states, all_attentions] if v is not None)\n return BaseModelOutput(\n last_hidden_state=output_states, hidden_states=all_hidden_states, attentions=all_attentions\n )\n\n\ndef make_log_bucket_position(relative_pos, bucket_size, max_position):\n sign = np.sign(relative_pos)\n mid = bucket_size // 2\n abs_pos = np.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, np.abs(relative_pos))\n log_pos = np.ceil(np.log(abs_pos / mid) / np.log((max_position - 1) / mid) * (mid - 1)) + mid\n bucket_pos = np.where(abs_pos <= mid, relative_pos, log_pos * sign).astype(np.int)\n return bucket_pos\n\n\ndef build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1):\n \"\"\"\n Build relative position according to the query and key\n\n We assume the absolute position of query :math:`P_q` is range from (0, query_size) and the absolute position of key\n :math:`P_k` is range from (0, key_size), The relative positions from query to key is :math:`R_{q \\\\rightarrow k} =\n P_q - P_k`\n\n Args:\n query_size (int): the length of query\n key_size (int): the length of key\n bucket_size (int): the size of position bucket\n max_position (int): the maximum allowed absolute position\n\n Return:\n :obj:`torch.LongTensor`: A tensor with shape [1, query_size, key_size]\n\n \"\"\"\n q_ids = np.arange(0, query_size)\n k_ids = np.arange(0, key_size)\n rel_pos_ids = q_ids[:, None] - np.tile(k_ids, (q_ids.shape[0], 1))\n if bucket_size > 0 and max_position > 0:\n rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position)\n rel_pos_ids = torch.tensor(rel_pos_ids, dtype=torch.long)\n rel_pos_ids = rel_pos_ids[:query_size, :]\n rel_pos_ids = rel_pos_ids.unsqueeze(0)\n return rel_pos_ids\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.c2p_dynamic_expand\ndef c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):\n return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)])\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.p2c_dynamic_expand\ndef p2c_dynamic_expand(c2p_pos, query_layer, key_layer):\n return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)])\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.pos_dynamic_expand\ndef pos_dynamic_expand(pos_index, p2c_att, key_layer):\n return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2)))\n\n\nclass DisentangledSelfAttention(nn.Module):\n \"\"\"\n Disentangled self-attention module\n\n Parameters:\n config (:obj:`Deberta\n# ... truncated ...","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":true} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.ContextPooler","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.ContextPooler#L54-L73","kind":"class","name":"ContextPooler","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":54,"end_line":73,"context_start_line":34,"context_end_line":93,"code":"from transformers.modeling_utils import PreTrainedModel\nfrom transformers.utils import logging\nfrom transformers.models.deberta_v2.configuration_deberta_v2 import DebertaV2Config\n\n\nlogger = logging.get_logger(__name__)\n\n_CONFIG_FOR_DOC = \"DebertaV2Config\"\n_TOKENIZER_FOR_DOC = \"DebertaV2Tokenizer\"\n_CHECKPOINT_FOR_DOC = \"microsoft/deberta-v2-xlarge\"\n\nDEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST = [\n \"microsoft/deberta-v2-xlarge\",\n \"microsoft/deberta-v2-xxlarge\",\n \"microsoft/deberta-v2-xlarge-mnli\",\n \"microsoft/deberta-v2-xxlarge-mnli\",\n]\n\n\n# Copied from transformers.models.deberta.modeling_deberta.ContextPooler\nclass ContextPooler(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size)\n self.dropout = StableDropout(config.pooler_dropout)\n self.config = config\n\n def forward(self, hidden_states):\n # We \"pool\" the model by simply taking the hidden state corresponding\n # to the first token.\n\n context_token = hidden_states[:, 0]\n context_token = self.dropout(context_token)\n pooled_output = self.dense(context_token)\n pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output)\n return pooled_output\n\n @property\n def output_dim(self):\n return self.config.hidden_size\n\n\n# Copied from transformers.models.deberta.modeling_deberta.XSoftmax with deberta->deberta_v2\nclass XSoftmax(torch.autograd.Function):\n \"\"\"\n Masked Softmax which is optimized for saving memory\n\n Args:\n input (:obj:`torch.tensor`): The input tensor that will apply softmax.\n mask (:obj:`torch.IntTensor`): The mask matrix where 0 indicate that element will be ignored in the softmax calculation.\n dim (int): The dimension that will apply softmax\n\n Example::\n\n >>> import torch\n >>> from transformers.models.deberta_v2.modeling_deberta_v2 import XSoftmax\n\n >>> # Make a tensor\n >>> x = torch.randn([4,20,100])\n","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.XSoftmax","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.XSoftmax#L77-L115","kind":"class","name":"XSoftmax","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":77,"end_line":115,"context_start_line":57,"context_end_line":135,"code":" self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size)\n self.dropout = StableDropout(config.pooler_dropout)\n self.config = config\n\n def forward(self, hidden_states):\n # We \"pool\" the model by simply taking the hidden state corresponding\n # to the first token.\n\n context_token = hidden_states[:, 0]\n context_token = self.dropout(context_token)\n pooled_output = self.dense(context_token)\n pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output)\n return pooled_output\n\n @property\n def output_dim(self):\n return self.config.hidden_size\n\n\n# Copied from transformers.models.deberta.modeling_deberta.XSoftmax with deberta->deberta_v2\nclass XSoftmax(torch.autograd.Function):\n \"\"\"\n Masked Softmax which is optimized for saving memory\n\n Args:\n input (:obj:`torch.tensor`): The input tensor that will apply softmax.\n mask (:obj:`torch.IntTensor`): The mask matrix where 0 indicate that element will be ignored in the softmax calculation.\n dim (int): The dimension that will apply softmax\n\n Example::\n\n >>> import torch\n >>> from transformers.models.deberta_v2.modeling_deberta_v2 import XSoftmax\n\n >>> # Make a tensor\n >>> x = torch.randn([4,20,100])\n\n >>> # Create a mask\n >>> mask = (x>0).int()\n\n >>> y = XSoftmax.apply(x, mask, dim=-1)\n \"\"\"\n\n @staticmethod\n def forward(self, input, mask, dim):\n self.dim = dim\n rmask = ~(mask.bool())\n\n output = input.masked_fill(rmask, float(\"-inf\"))\n output = torch.softmax(output, self.dim)\n output.masked_fill_(rmask, 0)\n self.save_for_backward(output)\n return output\n\n @staticmethod\n def backward(self, grad_output):\n (output,) = self.saved_tensors\n inputGrad = _softmax_backward_data(grad_output, output, self.dim, output)\n return inputGrad, None, None\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DropoutContext\nclass DropoutContext(object):\n def __init__(self):\n self.dropout = 0\n self.mask = None\n self.scale = 1\n self.reuse_mask = True\n\n\n# Copied from transformers.models.deberta.modeling_deberta.get_mask\ndef get_mask(input, local_context):\n if not isinstance(local_context, DropoutContext):\n dropout = local_context\n mask = None\n else:\n dropout = local_context.dropout\n dropout *= local_context.scale\n mask = local_context.mask if local_context.reuse_mask else None","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DropoutContext","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DropoutContext#L119-L124","kind":"class","name":"DropoutContext","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":119,"end_line":124,"context_start_line":99,"context_end_line":144,"code":"\n @staticmethod\n def forward(self, input, mask, dim):\n self.dim = dim\n rmask = ~(mask.bool())\n\n output = input.masked_fill(rmask, float(\"-inf\"))\n output = torch.softmax(output, self.dim)\n output.masked_fill_(rmask, 0)\n self.save_for_backward(output)\n return output\n\n @staticmethod\n def backward(self, grad_output):\n (output,) = self.saved_tensors\n inputGrad = _softmax_backward_data(grad_output, output, self.dim, output)\n return inputGrad, None, None\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DropoutContext\nclass DropoutContext(object):\n def __init__(self):\n self.dropout = 0\n self.mask = None\n self.scale = 1\n self.reuse_mask = True\n\n\n# Copied from transformers.models.deberta.modeling_deberta.get_mask\ndef get_mask(input, local_context):\n if not isinstance(local_context, DropoutContext):\n dropout = local_context\n mask = None\n else:\n dropout = local_context.dropout\n dropout *= local_context.scale\n mask = local_context.mask if local_context.reuse_mask else None\n\n if dropout > 0 and mask is None:\n mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).bool()\n\n if isinstance(local_context, DropoutContext):\n if local_context.mask is None:\n local_context.mask = mask\n\n return mask, dropout","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.get_mask","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.get_mask#L128-L144","kind":"function","name":"get_mask","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":128,"end_line":144,"context_start_line":108,"context_end_line":164,"code":" self.save_for_backward(output)\n return output\n\n @staticmethod\n def backward(self, grad_output):\n (output,) = self.saved_tensors\n inputGrad = _softmax_backward_data(grad_output, output, self.dim, output)\n return inputGrad, None, None\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DropoutContext\nclass DropoutContext(object):\n def __init__(self):\n self.dropout = 0\n self.mask = None\n self.scale = 1\n self.reuse_mask = True\n\n\n# Copied from transformers.models.deberta.modeling_deberta.get_mask\ndef get_mask(input, local_context):\n if not isinstance(local_context, DropoutContext):\n dropout = local_context\n mask = None\n else:\n dropout = local_context.dropout\n dropout *= local_context.scale\n mask = local_context.mask if local_context.reuse_mask else None\n\n if dropout > 0 and mask is None:\n mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).bool()\n\n if isinstance(local_context, DropoutContext):\n if local_context.mask is None:\n local_context.mask = mask\n\n return mask, dropout\n\n\n# Copied from transformers.models.deberta.modeling_deberta.XDropout\nclass XDropout(torch.autograd.Function):\n \"\"\"Optimized dropout function to save computation and memory by using mask operation instead of multiplication.\"\"\"\n\n @staticmethod\n def forward(ctx, input, local_ctx):\n mask, dropout = get_mask(input, local_ctx)\n ctx.scale = 1.0 / (1 - dropout)\n if dropout > 0:\n ctx.save_for_backward(mask)\n return input.masked_fill(mask, 0) * ctx.scale\n else:\n return input\n\n @staticmethod\n def backward(ctx, grad_output):\n if ctx.scale > 1:\n (mask,) = ctx.saved_tensors","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.XDropout","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.XDropout#L148-L167","kind":"class","name":"XDropout","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":148,"end_line":167,"context_start_line":128,"context_end_line":187,"code":"def get_mask(input, local_context):\n if not isinstance(local_context, DropoutContext):\n dropout = local_context\n mask = None\n else:\n dropout = local_context.dropout\n dropout *= local_context.scale\n mask = local_context.mask if local_context.reuse_mask else None\n\n if dropout > 0 and mask is None:\n mask = (1 - torch.empty_like(input).bernoulli_(1 - dropout)).bool()\n\n if isinstance(local_context, DropoutContext):\n if local_context.mask is None:\n local_context.mask = mask\n\n return mask, dropout\n\n\n# Copied from transformers.models.deberta.modeling_deberta.XDropout\nclass XDropout(torch.autograd.Function):\n \"\"\"Optimized dropout function to save computation and memory by using mask operation instead of multiplication.\"\"\"\n\n @staticmethod\n def forward(ctx, input, local_ctx):\n mask, dropout = get_mask(input, local_ctx)\n ctx.scale = 1.0 / (1 - dropout)\n if dropout > 0:\n ctx.save_for_backward(mask)\n return input.masked_fill(mask, 0) * ctx.scale\n else:\n return input\n\n @staticmethod\n def backward(ctx, grad_output):\n if ctx.scale > 1:\n (mask,) = ctx.saved_tensors\n return grad_output.masked_fill(mask, 0) * ctx.scale, None\n else:\n return grad_output, None\n\n\n# Copied from transformers.models.deberta.modeling_deberta.StableDropout\nclass StableDropout(nn.Module):\n \"\"\"\n Optimized dropout module for stabilizing the training\n\n Args:\n drop_prob (float): the dropout probabilities\n \"\"\"\n\n def __init__(self, drop_prob):\n super().__init__()\n self.drop_prob = drop_prob\n self.count = 0\n self.context_stack = None\n\n def forward(self, x):\n \"\"\"\n Call the module","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.StableDropout","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.StableDropout#L171-L217","kind":"class","name":"StableDropout","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":171,"end_line":217,"context_start_line":151,"context_end_line":237,"code":" @staticmethod\n def forward(ctx, input, local_ctx):\n mask, dropout = get_mask(input, local_ctx)\n ctx.scale = 1.0 / (1 - dropout)\n if dropout > 0:\n ctx.save_for_backward(mask)\n return input.masked_fill(mask, 0) * ctx.scale\n else:\n return input\n\n @staticmethod\n def backward(ctx, grad_output):\n if ctx.scale > 1:\n (mask,) = ctx.saved_tensors\n return grad_output.masked_fill(mask, 0) * ctx.scale, None\n else:\n return grad_output, None\n\n\n# Copied from transformers.models.deberta.modeling_deberta.StableDropout\nclass StableDropout(nn.Module):\n \"\"\"\n Optimized dropout module for stabilizing the training\n\n Args:\n drop_prob (float): the dropout probabilities\n \"\"\"\n\n def __init__(self, drop_prob):\n super().__init__()\n self.drop_prob = drop_prob\n self.count = 0\n self.context_stack = None\n\n def forward(self, x):\n \"\"\"\n Call the module\n\n Args:\n x (:obj:`torch.tensor`): The input tensor to apply dropout\n \"\"\"\n if self.training and self.drop_prob > 0:\n return XDropout.apply(x, self.get_context())\n return x\n\n def clear_context(self):\n self.count = 0\n self.context_stack = None\n\n def init_context(self, reuse_mask=True, scale=1):\n if self.context_stack is None:\n self.context_stack = []\n self.count = 0\n for c in self.context_stack:\n c.reuse_mask = reuse_mask\n c.scale = scale\n\n def get_context(self):\n if self.context_stack is not None:\n if self.count >= len(self.context_stack):\n self.context_stack.append(DropoutContext())\n ctx = self.context_stack[self.count]\n ctx.dropout = self.drop_prob\n self.count += 1\n return ctx\n else:\n return self.drop_prob\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm\nclass DebertaV2SelfOutput(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)\n self.dropout = StableDropout(config.hidden_dropout_prob)\n\n def forward(self, hidden_states, input_tensor):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.LayerNorm(hidden_states + input_tensor)\n return hidden_states\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2\nclass DebertaV2Attention(nn.Module):\n def __init__(self, config):","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2SelfOutput","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2SelfOutput#L221-L232","kind":"class","name":"DebertaV2SelfOutput","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":221,"end_line":232,"context_start_line":201,"context_end_line":252,"code":" if self.context_stack is None:\n self.context_stack = []\n self.count = 0\n for c in self.context_stack:\n c.reuse_mask = reuse_mask\n c.scale = scale\n\n def get_context(self):\n if self.context_stack is not None:\n if self.count >= len(self.context_stack):\n self.context_stack.append(DropoutContext())\n ctx = self.context_stack[self.count]\n ctx.dropout = self.drop_prob\n self.count += 1\n return ctx\n else:\n return self.drop_prob\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm\nclass DebertaV2SelfOutput(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)\n self.dropout = StableDropout(config.hidden_dropout_prob)\n\n def forward(self, hidden_states, input_tensor):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.LayerNorm(hidden_states + input_tensor)\n return hidden_states\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2\nclass DebertaV2Attention(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.self = DisentangledSelfAttention(config)\n self.output = DebertaV2SelfOutput(config)\n self.config = config\n\n def forward(\n self,\n hidden_states,\n attention_mask,\n return_att=False,\n query_states=None,\n relative_pos=None,\n rel_embeddings=None,\n ):\n self_output = self.self(","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2Attention","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2Attention#L236-L269","kind":"class","name":"DebertaV2Attention","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":236,"end_line":269,"context_start_line":216,"context_end_line":289,"code":" else:\n return self.drop_prob\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm\nclass DebertaV2SelfOutput(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)\n self.dropout = StableDropout(config.hidden_dropout_prob)\n\n def forward(self, hidden_states, input_tensor):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.LayerNorm(hidden_states + input_tensor)\n return hidden_states\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2\nclass DebertaV2Attention(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.self = DisentangledSelfAttention(config)\n self.output = DebertaV2SelfOutput(config)\n self.config = config\n\n def forward(\n self,\n hidden_states,\n attention_mask,\n return_att=False,\n query_states=None,\n relative_pos=None,\n rel_embeddings=None,\n ):\n self_output = self.self(\n hidden_states,\n attention_mask,\n return_att,\n query_states=query_states,\n relative_pos=relative_pos,\n rel_embeddings=rel_embeddings,\n )\n if return_att:\n self_output, att_matrix = self_output\n if query_states is None:\n query_states = hidden_states\n attention_output = self.output(self_output, query_states)\n\n if return_att:\n return (attention_output, att_matrix)\n else:\n return attention_output\n\n\n# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->DebertaV2\nclass DebertaV2Intermediate(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.intermediate_size)\n if isinstance(config.hidden_act, str):\n self.intermediate_act_fn = ACT2FN[config.hidden_act]\n else:\n self.intermediate_act_fn = config.hidden_act\n\n def forward(self, hidden_states):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.intermediate_act_fn(hidden_states)\n return hidden_states\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaOutput with DebertaLayerNorm->LayerNorm\nclass DebertaV2Output(nn.Module):","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2Intermediate","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2Intermediate#L273-L285","kind":"class","name":"DebertaV2Intermediate","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":273,"end_line":285,"context_start_line":253,"context_end_line":305,"code":" hidden_states,\n attention_mask,\n return_att,\n query_states=query_states,\n relative_pos=relative_pos,\n rel_embeddings=rel_embeddings,\n )\n if return_att:\n self_output, att_matrix = self_output\n if query_states is None:\n query_states = hidden_states\n attention_output = self.output(self_output, query_states)\n\n if return_att:\n return (attention_output, att_matrix)\n else:\n return attention_output\n\n\n# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->DebertaV2\nclass DebertaV2Intermediate(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.intermediate_size)\n if isinstance(config.hidden_act, str):\n self.intermediate_act_fn = ACT2FN[config.hidden_act]\n else:\n self.intermediate_act_fn = config.hidden_act\n\n def forward(self, hidden_states):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.intermediate_act_fn(hidden_states)\n return hidden_states\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaOutput with DebertaLayerNorm->LayerNorm\nclass DebertaV2Output(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.intermediate_size, config.hidden_size)\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)\n self.dropout = StableDropout(config.hidden_dropout_prob)\n self.config = config\n\n def forward(self, hidden_states, input_tensor):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.LayerNorm(hidden_states + input_tensor)\n return hidden_states\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaLayer with Deberta->DebertaV2\nclass DebertaV2Layer(nn.Module):","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2Output","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2Output#L289-L301","kind":"class","name":"DebertaV2Output","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":289,"end_line":301,"context_start_line":269,"context_end_line":321,"code":" return attention_output\n\n\n# Copied from transformers.models.bert.modeling_bert.BertIntermediate with Bert->DebertaV2\nclass DebertaV2Intermediate(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.intermediate_size)\n if isinstance(config.hidden_act, str):\n self.intermediate_act_fn = ACT2FN[config.hidden_act]\n else:\n self.intermediate_act_fn = config.hidden_act\n\n def forward(self, hidden_states):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.intermediate_act_fn(hidden_states)\n return hidden_states\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaOutput with DebertaLayerNorm->LayerNorm\nclass DebertaV2Output(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.intermediate_size, config.hidden_size)\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)\n self.dropout = StableDropout(config.hidden_dropout_prob)\n self.config = config\n\n def forward(self, hidden_states, input_tensor):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.LayerNorm(hidden_states + input_tensor)\n return hidden_states\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaLayer with Deberta->DebertaV2\nclass DebertaV2Layer(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.attention = DebertaV2Attention(config)\n self.intermediate = DebertaV2Intermediate(config)\n self.output = DebertaV2Output(config)\n\n def forward(\n self,\n hidden_states,\n attention_mask,\n return_att=False,\n query_states=None,\n relative_pos=None,\n rel_embeddings=None,\n ):\n attention_output = self.attention(","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2Layer","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2Layer#L305-L336","kind":"class","name":"DebertaV2Layer","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":305,"end_line":336,"context_start_line":285,"context_end_line":356,"code":" return hidden_states\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaOutput with DebertaLayerNorm->LayerNorm\nclass DebertaV2Output(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.intermediate_size, config.hidden_size)\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)\n self.dropout = StableDropout(config.hidden_dropout_prob)\n self.config = config\n\n def forward(self, hidden_states, input_tensor):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.LayerNorm(hidden_states + input_tensor)\n return hidden_states\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaLayer with Deberta->DebertaV2\nclass DebertaV2Layer(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.attention = DebertaV2Attention(config)\n self.intermediate = DebertaV2Intermediate(config)\n self.output = DebertaV2Output(config)\n\n def forward(\n self,\n hidden_states,\n attention_mask,\n return_att=False,\n query_states=None,\n relative_pos=None,\n rel_embeddings=None,\n ):\n attention_output = self.attention(\n hidden_states,\n attention_mask,\n return_att=return_att,\n query_states=query_states,\n relative_pos=relative_pos,\n rel_embeddings=rel_embeddings,\n )\n if return_att:\n attention_output, att_matrix = attention_output\n intermediate_output = self.intermediate(attention_output)\n layer_output = self.output(intermediate_output, attention_output)\n if return_att:\n return (layer_output, att_matrix)\n else:\n return layer_output\n\n\nclass ConvLayer(nn.Module):\n def __init__(self, config):\n super().__init__()\n kernel_size = getattr(config, \"conv_kernel_size\", 3)\n groups = getattr(config, \"conv_groups\", 1)\n self.conv_act = getattr(config, \"conv_act\", \"tanh\")\n self.conv = nn.Conv1d(\n config.hidden_size, config.hidden_size, kernel_size, padding=(kernel_size - 1) // 2, groups=groups\n )\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)\n self.dropout = StableDropout(config.hidden_dropout_prob)\n self.config = config\n\n def forward(self, hidden_states, residual_states, input_mask):\n out = self.conv(hidden_states.permute(0, 2, 1).contiguous()).permute(0, 2, 1).contiguous()\n rmask = (1 - input_mask).bool()\n out.masked_fill_(rmask.unsqueeze(-1).expand(out.size()), 0)\n out = ACT2FN[self.conv_act](self.dropout(out))","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.ConvLayer","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.ConvLayer#L339-L372","kind":"class","name":"ConvLayer","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":339,"end_line":372,"context_start_line":319,"context_end_line":392,"code":" rel_embeddings=None,\n ):\n attention_output = self.attention(\n hidden_states,\n attention_mask,\n return_att=return_att,\n query_states=query_states,\n relative_pos=relative_pos,\n rel_embeddings=rel_embeddings,\n )\n if return_att:\n attention_output, att_matrix = attention_output\n intermediate_output = self.intermediate(attention_output)\n layer_output = self.output(intermediate_output, attention_output)\n if return_att:\n return (layer_output, att_matrix)\n else:\n return layer_output\n\n\nclass ConvLayer(nn.Module):\n def __init__(self, config):\n super().__init__()\n kernel_size = getattr(config, \"conv_kernel_size\", 3)\n groups = getattr(config, \"conv_groups\", 1)\n self.conv_act = getattr(config, \"conv_act\", \"tanh\")\n self.conv = nn.Conv1d(\n config.hidden_size, config.hidden_size, kernel_size, padding=(kernel_size - 1) // 2, groups=groups\n )\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)\n self.dropout = StableDropout(config.hidden_dropout_prob)\n self.config = config\n\n def forward(self, hidden_states, residual_states, input_mask):\n out = self.conv(hidden_states.permute(0, 2, 1).contiguous()).permute(0, 2, 1).contiguous()\n rmask = (1 - input_mask).bool()\n out.masked_fill_(rmask.unsqueeze(-1).expand(out.size()), 0)\n out = ACT2FN[self.conv_act](self.dropout(out))\n\n layer_norm_input = residual_states + out\n output = self.LayerNorm(layer_norm_input).to(layer_norm_input)\n\n if input_mask is None:\n output_states = output\n else:\n if input_mask.dim() != layer_norm_input.dim():\n if input_mask.dim() == 4:\n input_mask = input_mask.squeeze(1).squeeze(1)\n input_mask = input_mask.unsqueeze(2)\n\n input_mask = input_mask.to(output.dtype)\n output_states = output * input_mask\n\n return output_states\n\n\nclass DebertaV2Encoder(nn.Module):\n \"\"\"Modified BertEncoder with relative position bias support\"\"\"\n\n def __init__(self, config):\n super().__init__()\n\n self.layer = nn.ModuleList([DebertaV2Layer(config) for _ in range(config.num_hidden_layers)])\n self.relative_attention = getattr(config, \"relative_attention\", False)\n\n if self.relative_attention:\n self.max_relative_positions = getattr(config, \"max_relative_positions\", -1)\n if self.max_relative_positions < 1:\n self.max_relative_positions = config.max_position_embeddings\n\n self.position_buckets = getattr(config, \"position_buckets\", -1)\n pos_ebd_size = self.max_relative_positions * 2\n\n if self.position_buckets > 0:","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2Encoder","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2Encoder#L375-L490","kind":"class","name":"DebertaV2Encoder","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":375,"end_line":490,"context_start_line":355,"context_end_line":510,"code":" out.masked_fill_(rmask.unsqueeze(-1).expand(out.size()), 0)\n out = ACT2FN[self.conv_act](self.dropout(out))\n\n layer_norm_input = residual_states + out\n output = self.LayerNorm(layer_norm_input).to(layer_norm_input)\n\n if input_mask is None:\n output_states = output\n else:\n if input_mask.dim() != layer_norm_input.dim():\n if input_mask.dim() == 4:\n input_mask = input_mask.squeeze(1).squeeze(1)\n input_mask = input_mask.unsqueeze(2)\n\n input_mask = input_mask.to(output.dtype)\n output_states = output * input_mask\n\n return output_states\n\n\nclass DebertaV2Encoder(nn.Module):\n \"\"\"Modified BertEncoder with relative position bias support\"\"\"\n\n def __init__(self, config):\n super().__init__()\n\n self.layer = nn.ModuleList([DebertaV2Layer(config) for _ in range(config.num_hidden_layers)])\n self.relative_attention = getattr(config, \"relative_attention\", False)\n\n if self.relative_attention:\n self.max_relative_positions = getattr(config, \"max_relative_positions\", -1)\n if self.max_relative_positions < 1:\n self.max_relative_positions = config.max_position_embeddings\n\n self.position_buckets = getattr(config, \"position_buckets\", -1)\n pos_ebd_size = self.max_relative_positions * 2\n\n if self.position_buckets > 0:\n pos_ebd_size = self.position_buckets * 2\n\n self.rel_embeddings = nn.Embedding(pos_ebd_size, config.hidden_size)\n\n self.norm_rel_ebd = [x.strip() for x in getattr(config, \"norm_rel_ebd\", \"none\").lower().split(\"|\")]\n\n if \"layer_norm\" in self.norm_rel_ebd:\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True)\n\n self.conv = ConvLayer(config) if getattr(config, \"conv_kernel_size\", 0) > 0 else None\n\n def get_rel_embedding(self):\n rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None\n if rel_embeddings is not None and (\"layer_norm\" in self.norm_rel_ebd):\n rel_embeddings = self.LayerNorm(rel_embeddings)\n return rel_embeddings\n\n def get_attention_mask(self, attention_mask):\n if attention_mask.dim() <= 2:\n extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)\n attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1)\n attention_mask = attention_mask.byte()\n elif attention_mask.dim() == 3:\n attention_mask = attention_mask.unsqueeze(1)\n\n return attention_mask\n\n def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None):\n if self.relative_attention and relative_pos is None:\n q = query_states.size(-2) if query_states is not None else hidden_states.size(-2)\n relative_pos = build_relative_position(\n q, hidden_states.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions\n )\n return relative_pos\n\n def forward(\n self,\n hidden_states,\n attention_mask,\n output_hidden_states=True,\n output_attentions=False,\n query_states=None,\n relative_pos=None,\n return_dict=True,\n ):\n if attention_mask.dim() <= 2:\n input_mask = attention_mask\n else:\n input_mask = (attention_mask.sum(-2) > 0).byte()\n attention_mask = self.get_attention_mask(attention_mask)\n relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos)\n\n all_hidden_states = () if output_hidden_states else None\n all_attentions = () if output_attentions else None\n\n if isinstance(hidden_states, Sequence):\n next_kv = hidden_states[0]\n else:\n next_kv = hidden_states\n rel_embeddings = self.get_rel_embedding()\n output_states = next_kv\n for i, layer_module in enumerate(self.layer):\n\n if output_hidden_states:\n all_hidden_states = all_hidden_states + (output_states,)\n\n output_states = layer_module(\n next_kv,\n attention_mask,\n output_attentions,\n query_states=query_states,\n relative_pos=relative_pos,\n rel_embeddings=rel_embeddings,\n )\n if output_attentions:\n output_states, att_m = output_states\n\n if i == 0 and self.conv is not None:\n output_states = self.conv(hidden_states, output_states, input_mask)\n\n if query_states is not None:\n query_states = output_states\n if isinstance(hidden_states, Sequence):\n next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None\n else:\n next_kv = output_states\n\n if output_attentions:\n all_attentions = all_attentions + (att_m,)\n\n if output_hidden_states:\n all_hidden_states = all_hidden_states + (output_states,)\n\n if not return_dict:\n return tuple(v for v in [output_states, all_hidden_states, all_attentions] if v is not None)\n return BaseModelOutput(\n last_hidden_state=output_states, hidden_states=all_hidden_states, attentions=all_attentions\n )\n\n\ndef make_log_bucket_position(relative_pos, bucket_size, max_position):\n sign = np.sign(relative_pos)\n mid = bucket_size // 2\n abs_pos = np.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, np.abs(relative_pos))\n log_pos = np.ceil(np.log(abs_pos / mid) / np.log((max_position - 1) / mid) * (mid - 1)) + mid\n bucket_pos = np.where(abs_pos <= mid, relative_pos, log_pos * sign).astype(np.int)\n return bucket_pos\n\n\ndef build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1):\n \"\"\"\n Build relative position according to the query and key\n\n We assume the absolute position of query :math:`P_q` is range from (0, query_size) and the absolute position of key\n :math:`P_k` is range from (0, key_size), The relative positions from query to key is :math:`R_{q \\\\rightarrow k} =\n P_q - P_k`\n\n Args:","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.make_log_bucket_position","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.make_log_bucket_position#L493-L499","kind":"function","name":"make_log_bucket_position","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":493,"end_line":499,"context_start_line":473,"context_end_line":519,"code":" if query_states is not None:\n query_states = output_states\n if isinstance(hidden_states, Sequence):\n next_kv = hidden_states[i + 1] if i + 1 < len(self.layer) else None\n else:\n next_kv = output_states\n\n if output_attentions:\n all_attentions = all_attentions + (att_m,)\n\n if output_hidden_states:\n all_hidden_states = all_hidden_states + (output_states,)\n\n if not return_dict:\n return tuple(v for v in [output_states, all_hidden_states, all_attentions] if v is not None)\n return BaseModelOutput(\n last_hidden_state=output_states, hidden_states=all_hidden_states, attentions=all_attentions\n )\n\n\ndef make_log_bucket_position(relative_pos, bucket_size, max_position):\n sign = np.sign(relative_pos)\n mid = bucket_size // 2\n abs_pos = np.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, np.abs(relative_pos))\n log_pos = np.ceil(np.log(abs_pos / mid) / np.log((max_position - 1) / mid) * (mid - 1)) + mid\n bucket_pos = np.where(abs_pos <= mid, relative_pos, log_pos * sign).astype(np.int)\n return bucket_pos\n\n\ndef build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1):\n \"\"\"\n Build relative position according to the query and key\n\n We assume the absolute position of query :math:`P_q` is range from (0, query_size) and the absolute position of key\n :math:`P_k` is range from (0, key_size), The relative positions from query to key is :math:`R_{q \\\\rightarrow k} =\n P_q - P_k`\n\n Args:\n query_size (int): the length of query\n key_size (int): the length of key\n bucket_size (int): the size of position bucket\n max_position (int): the maximum allowed absolute position\n\n Return:\n :obj:`torch.LongTensor`: A tensor with shape [1, query_size, key_size]\n\n \"\"\"","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.build_relative_position","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.build_relative_position#L502-L528","kind":"function","name":"build_relative_position","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":502,"end_line":528,"context_start_line":482,"context_end_line":548,"code":"\n if output_hidden_states:\n all_hidden_states = all_hidden_states + (output_states,)\n\n if not return_dict:\n return tuple(v for v in [output_states, all_hidden_states, all_attentions] if v is not None)\n return BaseModelOutput(\n last_hidden_state=output_states, hidden_states=all_hidden_states, attentions=all_attentions\n )\n\n\ndef make_log_bucket_position(relative_pos, bucket_size, max_position):\n sign = np.sign(relative_pos)\n mid = bucket_size // 2\n abs_pos = np.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, np.abs(relative_pos))\n log_pos = np.ceil(np.log(abs_pos / mid) / np.log((max_position - 1) / mid) * (mid - 1)) + mid\n bucket_pos = np.where(abs_pos <= mid, relative_pos, log_pos * sign).astype(np.int)\n return bucket_pos\n\n\ndef build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1):\n \"\"\"\n Build relative position according to the query and key\n\n We assume the absolute position of query :math:`P_q` is range from (0, query_size) and the absolute position of key\n :math:`P_k` is range from (0, key_size), The relative positions from query to key is :math:`R_{q \\\\rightarrow k} =\n P_q - P_k`\n\n Args:\n query_size (int): the length of query\n key_size (int): the length of key\n bucket_size (int): the size of position bucket\n max_position (int): the maximum allowed absolute position\n\n Return:\n :obj:`torch.LongTensor`: A tensor with shape [1, query_size, key_size]\n\n \"\"\"\n q_ids = np.arange(0, query_size)\n k_ids = np.arange(0, key_size)\n rel_pos_ids = q_ids[:, None] - np.tile(k_ids, (q_ids.shape[0], 1))\n if bucket_size > 0 and max_position > 0:\n rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position)\n rel_pos_ids = torch.tensor(rel_pos_ids, dtype=torch.long)\n rel_pos_ids = rel_pos_ids[:query_size, :]\n rel_pos_ids = rel_pos_ids.unsqueeze(0)\n return rel_pos_ids\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.c2p_dynamic_expand\ndef c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):\n return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)])\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.p2c_dynamic_expand\ndef p2c_dynamic_expand(c2p_pos, query_layer, key_layer):\n return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)])\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.pos_dynamic_expand\ndef pos_dynamic_expand(pos_index, p2c_att, key_layer):\n return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2)))\n\n","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.c2p_dynamic_expand","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.c2p_dynamic_expand#L533-L534","kind":"function","name":"c2p_dynamic_expand","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":533,"end_line":534,"context_start_line":513,"context_end_line":554,"code":" bucket_size (int): the size of position bucket\n max_position (int): the maximum allowed absolute position\n\n Return:\n :obj:`torch.LongTensor`: A tensor with shape [1, query_size, key_size]\n\n \"\"\"\n q_ids = np.arange(0, query_size)\n k_ids = np.arange(0, key_size)\n rel_pos_ids = q_ids[:, None] - np.tile(k_ids, (q_ids.shape[0], 1))\n if bucket_size > 0 and max_position > 0:\n rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position)\n rel_pos_ids = torch.tensor(rel_pos_ids, dtype=torch.long)\n rel_pos_ids = rel_pos_ids[:query_size, :]\n rel_pos_ids = rel_pos_ids.unsqueeze(0)\n return rel_pos_ids\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.c2p_dynamic_expand\ndef c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):\n return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)])\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.p2c_dynamic_expand\ndef p2c_dynamic_expand(c2p_pos, query_layer, key_layer):\n return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)])\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.pos_dynamic_expand\ndef pos_dynamic_expand(pos_index, p2c_att, key_layer):\n return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2)))\n\n\nclass DisentangledSelfAttention(nn.Module):\n \"\"\"\n Disentangled self-attention module\n\n Parameters:\n config (:obj:`DebertaV2Config`):","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.p2c_dynamic_expand","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.p2c_dynamic_expand#L539-L540","kind":"function","name":"p2c_dynamic_expand","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":539,"end_line":540,"context_start_line":519,"context_end_line":560,"code":" \"\"\"\n q_ids = np.arange(0, query_size)\n k_ids = np.arange(0, key_size)\n rel_pos_ids = q_ids[:, None] - np.tile(k_ids, (q_ids.shape[0], 1))\n if bucket_size > 0 and max_position > 0:\n rel_pos_ids = make_log_bucket_position(rel_pos_ids, bucket_size, max_position)\n rel_pos_ids = torch.tensor(rel_pos_ids, dtype=torch.long)\n rel_pos_ids = rel_pos_ids[:query_size, :]\n rel_pos_ids = rel_pos_ids.unsqueeze(0)\n return rel_pos_ids\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.c2p_dynamic_expand\ndef c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):\n return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)])\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.p2c_dynamic_expand\ndef p2c_dynamic_expand(c2p_pos, query_layer, key_layer):\n return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)])\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.pos_dynamic_expand\ndef pos_dynamic_expand(pos_index, p2c_att, key_layer):\n return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2)))\n\n\nclass DisentangledSelfAttention(nn.Module):\n \"\"\"\n Disentangled self-attention module\n\n Parameters:\n config (:obj:`DebertaV2Config`):\n A model config class instance with the configuration to build a new model. The schema is similar to\n `BertConfig`, for more details, please refer :class:`~transformers.DebertaV2Config`\n\n \"\"\"\n\n def __init__(self, config):","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.pos_dynamic_expand","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.pos_dynamic_expand#L545-L546","kind":"function","name":"pos_dynamic_expand","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":545,"end_line":546,"context_start_line":525,"context_end_line":566,"code":" rel_pos_ids = torch.tensor(rel_pos_ids, dtype=torch.long)\n rel_pos_ids = rel_pos_ids[:query_size, :]\n rel_pos_ids = rel_pos_ids.unsqueeze(0)\n return rel_pos_ids\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.c2p_dynamic_expand\ndef c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):\n return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)])\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.p2c_dynamic_expand\ndef p2c_dynamic_expand(c2p_pos, query_layer, key_layer):\n return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)])\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.pos_dynamic_expand\ndef pos_dynamic_expand(pos_index, p2c_att, key_layer):\n return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2)))\n\n\nclass DisentangledSelfAttention(nn.Module):\n \"\"\"\n Disentangled self-attention module\n\n Parameters:\n config (:obj:`DebertaV2Config`):\n A model config class instance with the configuration to build a new model. The schema is similar to\n `BertConfig`, for more details, please refer :class:`~transformers.DebertaV2Config`\n\n \"\"\"\n\n def __init__(self, config):\n super().__init__()\n if config.hidden_size % config.num_attention_heads != 0:\n raise ValueError(\n f\"The hidden size ({config.hidden_size}) is not a multiple of the number of attention \"\n f\"heads ({config.num_attention_heads})\"\n )","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DisentangledSelfAttention","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DisentangledSelfAttention#L549-L794","kind":"class","name":"DisentangledSelfAttention","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":549,"end_line":794,"context_start_line":529,"context_end_line":814,"code":"\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.c2p_dynamic_expand\ndef c2p_dynamic_expand(c2p_pos, query_layer, relative_pos):\n return c2p_pos.expand([query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)])\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.p2c_dynamic_expand\ndef p2c_dynamic_expand(c2p_pos, query_layer, key_layer):\n return c2p_pos.expand([query_layer.size(0), query_layer.size(1), key_layer.size(-2), key_layer.size(-2)])\n\n\n@torch.jit.script\n# Copied from transformers.models.deberta.modeling_deberta.pos_dynamic_expand\ndef pos_dynamic_expand(pos_index, p2c_att, key_layer):\n return pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2)))\n\n\nclass DisentangledSelfAttention(nn.Module):\n \"\"\"\n Disentangled self-attention module\n\n Parameters:\n config (:obj:`DebertaV2Config`):\n A model config class instance with the configuration to build a new model. The schema is similar to\n `BertConfig`, for more details, please refer :class:`~transformers.DebertaV2Config`\n\n \"\"\"\n\n def __init__(self, config):\n super().__init__()\n if config.hidden_size % config.num_attention_heads != 0:\n raise ValueError(\n f\"The hidden size ({config.hidden_size}) is not a multiple of the number of attention \"\n f\"heads ({config.num_attention_heads})\"\n )\n self.num_attention_heads = config.num_attention_heads\n _attention_head_size = config.hidden_size // config.num_attention_heads\n self.attention_head_size = getattr(config, \"attention_head_size\", _attention_head_size)\n self.all_head_size = self.num_attention_heads * self.attention_head_size\n self.query_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)\n self.key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)\n self.value_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)\n\n self.share_att_key = getattr(config, \"share_att_key\", False)\n self.pos_att_type = config.pos_att_type if config.pos_att_type is not None else []\n self.relative_attention = getattr(config, \"relative_attention\", False)\n\n if self.relative_attention:\n self.position_buckets = getattr(config, \"position_buckets\", -1)\n self.max_relative_positions = getattr(config, \"max_relative_positions\", -1)\n if self.max_relative_positions < 1:\n self.max_relative_positions = config.max_position_embeddings\n self.pos_ebd_size = self.max_relative_positions\n if self.position_buckets > 0:\n self.pos_ebd_size = self.position_buckets\n\n self.pos_dropout = StableDropout(config.hidden_dropout_prob)\n\n if not self.share_att_key:\n if \"c2p\" in self.pos_att_type or \"p2p\" in self.pos_att_type:\n self.pos_key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)\n if \"p2c\" in self.pos_att_type or \"p2p\" in self.pos_att_type:\n self.pos_query_proj = nn.Linear(config.hidden_size, self.all_head_size)\n\n self.dropout = StableDropout(config.attention_probs_dropout_prob)\n\n def transpose_for_scores(self, x, attention_heads):\n new_x_shape = x.size()[:-1] + (attention_heads, -1)\n x = x.view(*new_x_shape)\n return x.permute(0, 2, 1, 3).contiguous().view(-1, x.size(1), x.size(-1))\n\n def forward(\n self,\n hidden_states,\n attention_mask,\n return_att=False,\n query_states=None,\n relative_pos=None,\n rel_embeddings=None,\n ):\n \"\"\"\n Call the module\n\n Args:\n hidden_states (:obj:`torch.FloatTensor`):\n Input states to the module usually the output from previous layer, it will be the Q,K and V in\n `Attention(Q,K,V)`\n\n attention_mask (:obj:`torch.ByteTensor`):\n An attention mask matrix of shape [`B`, `N`, `N`] where `B` is the batch size, `N` is the maximum\n sequence length in which element [i,j] = `1` means the `i` th token in the input can attend to the `j`\n th token.\n\n return_att (:obj:`bool`, optional):\n Whether return the attention matrix.\n\n query_states (:obj:`torch.FloatTensor`, optional):\n The `Q` state in `Attention(Q,K,V)`.\n\n relative_pos (:obj:`torch.LongTensor`):\n The relative position encoding between the tokens in the sequence. It's of shape [`B`, `N`, `N`] with\n values ranging in [`-max_relative_positions`, `max_relative_positions`].\n\n rel_embeddings (:obj:`torch.FloatTensor`):\n The embedding of relative distances. It's a tensor of shape [:math:`2 \\\\times\n \\\\text{max_relative_positions}`, `hidden_size`].\n\n\n \"\"\"\n if query_states is None:\n query_states = hidden_states\n query_layer = self.transpose_for_scores(self.query_proj(query_states), self.num_attention_heads)\n key_layer = self.transpose_for_scores(self.key_proj(hidden_states), self.num_attention_heads)\n value_layer = self.transpose_for_scores(self.value_proj(hidden_states), self.num_attention_heads)\n\n rel_att = None\n # Take the dot product between \"query\" and \"key\" to get the raw attention scores.\n scale_factor = 1\n if \"c2p\" in self.pos_att_type:\n scale_factor += 1\n if \"p2c\" in self.pos_att_type:\n scale_factor += 1\n if \"p2p\" in self.pos_att_type:\n scale_factor += 1\n scale = math.sqrt(query_layer.size(-1) * scale_factor)\n attention_scores = torch.bmm(query_layer, key_layer.transpose(-1, -2)) / scale\n if self.relative_attention:\n rel_embeddings = self.pos_dropout(rel_embeddings)\n rel_att = self.disentangled_attention_bias(\n query_layer, key_layer, relative_pos, rel_embeddings, scale_factor\n )\n\n if rel_att is not None:\n attention_scores = attention_scores + rel_att\n attention_scores = attention_scores\n attention_scores = attention_scores.view(\n -1, self.num_attention_heads, attention_scores.size(-2), attention_scores.size(-1)\n )\n\n # bsz x height x length x dimension\n attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1)\n attention_probs = self.dropout(attention_probs)\n context_layer = torch.bmm(\n attention_probs.view(-1, attention_probs.size(-2), attention_probs.size(-1)), value_layer\n )\n context_layer = (\n context_layer.view(-1, self.num_attention_heads, context_layer.size(-2), context_layer.size(-1))\n .permute(0, 2, 1, 3)\n .contiguous()\n )\n new_context_layer_shape = context_layer.size()[:-2] + (-1,)\n context_layer = context_layer.view(*new_context_layer_shape)\n if return_att:\n return (context_layer, attention_probs)\n else:\n return context_layer\n\n def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor):\n if relative_pos is None:\n q = query_layer.size(-2)\n relative_pos = build_relative_position(\n q, key_layer.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions\n )\n if relative_pos.dim() == 2:\n relative_pos = relative_pos.unsqueeze(0).unsqueeze(0)\n elif relative_pos.dim() == 3:\n relative_pos = relative_pos.unsqueeze(1)\n # bsz x height x query x key\n elif relative_pos.dim() != 4:\n raise ValueError(f\"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.dim()}\")\n\n att_span = self.pos_ebd_size\n relative_pos = relative_pos.long().to(query_layer.device)\n\n rel_embeddings = rel_embeddings[self.pos_ebd_size - att_span : self.pos_ebd_size + att_span, :].unsqueeze(0)\n if self.share_att_key:\n pos_query_layer = self.transpose_for_scores(\n self.query_proj(rel_embeddings), self.num_attention_heads\n ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1)\n pos_key_layer = self.transpose_for_scores(self.key_proj(rel_embeddings), self.num_attention_heads).repeat(\n query_layer.size(0) // self.num_attention_heads, 1, 1\n )\n else:\n if \"c2p\" in self.pos_att_type or \"p2p\" in self.pos_att_type:\n pos_key_layer = self.transpose_for_scores(\n self.pos_key_proj(rel_embeddings), self.num_attention_heads\n ).repeat(\n query_layer.size(0) // self.num_attention_heads, 1, 1\n ) # .split(self.all_head_size, dim=-1)\n if \"p2c\" in self.pos_att_type or \"p2p\" in self.pos_att_type:\n pos_query_layer = self.transpose_for_scores(\n self.pos_query_proj(rel_embeddings), self.num_attention_heads\n ).repeat(\n query_layer.size(0) // self.num_attention_heads, 1, 1\n ) # .split(self.all_head_size, dim=-1)\n\n score = 0\n # content->position\n if \"c2p\" in self.pos_att_type:\n scale = math.sqrt(pos_key_layer.size(-1) * scale_factor)\n c2p_att = torch.bmm(query_layer, pos_key_layer.transpose(-1, -2))\n c2p_pos = torch.clamp(relative_pos + att_span, 0, att_span * 2 - 1)\n c2p_att = torch.gather(\n c2p_att,\n dim=-1,\n index=c2p_pos.squeeze(0).expand([query_layer.size(0), query_layer.size(1), relative_pos.size(-1)]),\n )\n score += c2p_att / scale\n\n # position->content\n if \"p2c\" in self.pos_att_type or \"p2p\" in self.pos_att_type:\n scale = math.sqrt(pos_query_layer.size(-1) * scale_factor)\n if key_layer.size(-2) != query_layer.size(-2):\n r_pos = build_relative_position(\n key_layer.size(-2),\n key_layer.size(-2),\n bucket_size=self.position_buckets,\n max_position=self.max_relative_positions,\n ).to(query_layer.device)\n r_pos = r_pos.unsqueeze(0)\n else:\n r_pos = relative_pos\n\n p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1)\n if query_layer.size(-2) != key_layer.size(-2):\n pos_index = relative_pos[:, :, :, 0].unsqueeze(-1)\n\n if \"p2c\" in self.pos_att_type:\n p2c_att = torch.bmm(key_layer, pos_query_layer.transpose(-1, -2))\n p2c_att = torch.gather(\n p2c_att,\n dim=-1,\n index=p2c_pos.squeeze(0).expand([query_layer.size(0), key_layer.size(-2), key_layer.size(-2)]),\n ).transpose(-1, -2)\n if query_layer.size(-2) != key_layer.size(-2):\n p2c_att = torch.gather(\n p2c_att,\n dim=-2,\n index=pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2))),\n )\n score += p2c_att / scale\n\n # position->position\n if \"p2p\" in self.pos_att_type:\n pos_query = pos_query_layer[:, :, att_span:, :]\n p2p_att = torch.matmul(pos_query, pos_key_layer.transpose(-1, -2))\n p2p_att = p2p_att.expand(query_layer.size()[:2] + p2p_att.size()[2:])\n if query_layer.size(-2) != key_layer.size(-2):\n p2p_att = torch.gather(\n p2p_att,\n dim=-2,\n index=pos_index.expand(query_layer.size()[:2] + (pos_index.size(-2), p2p_att.size(-1))),\n )\n p2p_att = torch.gather(\n p2p_att,\n dim=-1,\n index=c2p_pos.expand(\n [query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)]\n ),\n )\n score += p2p_att\n\n return score\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaEmbeddings with DebertaLayerNorm->LayerNorm\nclass DebertaV2Embeddings(nn.Module):\n \"\"\"Construct the embeddings from word, position and token_type embeddings.\"\"\"\n\n def __init__(self, config):\n super().__init__()\n pad_token_id = getattr(config, \"pad_token_id\", 0)\n self.embedding_size = getattr(config, \"embedding_size\", config.hidden_size)\n self.word_embeddings = nn.Embedding(config.vocab_size, self.embedding_size, padding_idx=pad_token_id)\n\n self.position_biased_input = getattr(config, \"position_biased_input\", True)\n if not self.position_biased_input:\n self.position_embeddings = None\n else:\n self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.embedding_size)\n\n if config.type_vocab_size > 0:\n self.token_type_embeddings = nn.Embedding(config.type_vocab_size, self.embedding_size)","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2Embeddings","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2Embeddings#L798-L869","kind":"class","name":"DebertaV2Embeddings","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":798,"end_line":869,"context_start_line":778,"context_end_line":889,"code":" p2p_att = p2p_att.expand(query_layer.size()[:2] + p2p_att.size()[2:])\n if query_layer.size(-2) != key_layer.size(-2):\n p2p_att = torch.gather(\n p2p_att,\n dim=-2,\n index=pos_index.expand(query_layer.size()[:2] + (pos_index.size(-2), p2p_att.size(-1))),\n )\n p2p_att = torch.gather(\n p2p_att,\n dim=-1,\n index=c2p_pos.expand(\n [query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)]\n ),\n )\n score += p2p_att\n\n return score\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaEmbeddings with DebertaLayerNorm->LayerNorm\nclass DebertaV2Embeddings(nn.Module):\n \"\"\"Construct the embeddings from word, position and token_type embeddings.\"\"\"\n\n def __init__(self, config):\n super().__init__()\n pad_token_id = getattr(config, \"pad_token_id\", 0)\n self.embedding_size = getattr(config, \"embedding_size\", config.hidden_size)\n self.word_embeddings = nn.Embedding(config.vocab_size, self.embedding_size, padding_idx=pad_token_id)\n\n self.position_biased_input = getattr(config, \"position_biased_input\", True)\n if not self.position_biased_input:\n self.position_embeddings = None\n else:\n self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.embedding_size)\n\n if config.type_vocab_size > 0:\n self.token_type_embeddings = nn.Embedding(config.type_vocab_size, self.embedding_size)\n\n if self.embedding_size != config.hidden_size:\n self.embed_proj = nn.Linear(self.embedding_size, config.hidden_size, bias=False)\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)\n self.dropout = StableDropout(config.hidden_dropout_prob)\n self.config = config\n\n # position_ids (1, len position emb) is contiguous in memory and exported when serialized\n self.register_buffer(\"position_ids\", torch.arange(config.max_position_embeddings).expand((1, -1)))\n\n def forward(self, input_ids=None, token_type_ids=None, position_ids=None, mask=None, inputs_embeds=None):\n if input_ids is not None:\n input_shape = input_ids.size()\n else:\n input_shape = inputs_embeds.size()[:-1]\n\n seq_length = input_shape[1]\n\n if position_ids is None:\n position_ids = self.position_ids[:, :seq_length]\n\n if token_type_ids is None:\n token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)\n\n if inputs_embeds is None:\n inputs_embeds = self.word_embeddings(input_ids)\n\n if self.position_embeddings is not None:\n position_embeddings = self.position_embeddings(position_ids.long())\n else:\n position_embeddings = torch.zeros_like(inputs_embeds)\n\n embeddings = inputs_embeds\n if self.position_biased_input:\n embeddings += position_embeddings\n if self.config.type_vocab_size > 0:\n token_type_embeddings = self.token_type_embeddings(token_type_ids)\n embeddings += token_type_embeddings\n\n if self.embedding_size != self.config.hidden_size:\n embeddings = self.embed_proj(embeddings)\n\n embeddings = self.LayerNorm(embeddings)\n\n if mask is not None:\n if mask.dim() != embeddings.dim():\n if mask.dim() == 4:\n mask = mask.squeeze(1).squeeze(1)\n mask = mask.unsqueeze(2)\n mask = mask.to(embeddings.dtype)\n\n embeddings = embeddings * mask\n\n embeddings = self.dropout(embeddings)\n return embeddings\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaPreTrainedModel with Deberta->DebertaV2\n\nclass DebertaV2PreTrainedModel(PreTrainedModel):\n \"\"\"\n An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained\n models.\n \"\"\"\n\n config_class = DebertaV2Config\n base_model_prefix = \"deberta\"\n _keys_to_ignore_on_load_missing = [\"position_ids\"]\n _keys_to_ignore_on_load_unexpected = [\"position_embeddings\"]\n\n def __init__(self, config):\n super().__init__(config)\n self._register_load_state_dict_pre_hook(self._pre_load_hook)\n\n def _init_weights(self, module):","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2PreTrainedModel","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2PreTrainedModel#L874-L919","kind":"class","name":"DebertaV2PreTrainedModel","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":874,"end_line":919,"context_start_line":854,"context_end_line":939,"code":" if self.embedding_size != self.config.hidden_size:\n embeddings = self.embed_proj(embeddings)\n\n embeddings = self.LayerNorm(embeddings)\n\n if mask is not None:\n if mask.dim() != embeddings.dim():\n if mask.dim() == 4:\n mask = mask.squeeze(1).squeeze(1)\n mask = mask.unsqueeze(2)\n mask = mask.to(embeddings.dtype)\n\n embeddings = embeddings * mask\n\n embeddings = self.dropout(embeddings)\n return embeddings\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaPreTrainedModel with Deberta->DebertaV2\n\nclass DebertaV2PreTrainedModel(PreTrainedModel):\n \"\"\"\n An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained\n models.\n \"\"\"\n\n config_class = DebertaV2Config\n base_model_prefix = \"deberta\"\n _keys_to_ignore_on_load_missing = [\"position_ids\"]\n _keys_to_ignore_on_load_unexpected = [\"position_embeddings\"]\n\n def __init__(self, config):\n super().__init__(config)\n self._register_load_state_dict_pre_hook(self._pre_load_hook)\n\n def _init_weights(self, module):\n \"\"\"Initialize the weights.\"\"\"\n if isinstance(module, nn.Linear):\n # Slightly different from the TF version which uses truncated_normal for initialization\n # cf https://github.com/pytorch/pytorch/pull/5617\n module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n if module.bias is not None:\n module.bias.data.zero_()\n elif isinstance(module, nn.Embedding):\n module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n if module.padding_idx is not None:\n module.weight.data[module.padding_idx].zero_()\n\n def _pre_load_hook(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):\n \"\"\"\n Removes the classifier if it doesn't have the correct number of labels.\n \"\"\"\n self_state = self.state_dict()\n if (\n (\"classifier.weight\" in self_state)\n and (\"classifier.weight\" in state_dict)\n and self_state[\"classifier.weight\"].size() != state_dict[\"classifier.weight\"].size()\n ):\n logger.warning(\n f\"The checkpoint classifier head has a shape {state_dict['classifier.weight'].size()} and this model \"\n f\"classifier head has a shape {self_state['classifier.weight'].size()}. Ignoring the checkpoint \"\n f\"weights. You should train your model on new data.\"\n )\n del state_dict[\"classifier.weight\"]\n if \"classifier.bias\" in state_dict:\n del state_dict[\"classifier.bias\"]\n\n\n\nDEBERTA_START_DOCSTRING = r\"\"\"\n The DeBERTa model was proposed in `DeBERTa: Decoding-enhanced BERT with Disentangled Attention\n `_ by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It's build on top of\n BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two\n improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.\n\n This model is also a PyTorch `torch.nn.Module `__\n subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to\n general usage and behavior.```\n\n\n Parameters:\n config (:class:`~transformers.DebertaV2Config`): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model\n weights.\n\"\"\"","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2Model","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2Model#L992-L1100","kind":"class","name":"DebertaV2Model","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":992,"end_line":1100,"context_start_line":972,"context_end_line":1120,"code":" Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.\n This is useful if you want more control over how to convert `input_ids` indices into associated vectors\n than the model's internal embedding lookup matrix.\n output_attentions (:obj:`bool`, `optional`):\n Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned\n tensors for more detail.\n output_hidden_states (:obj:`bool`, `optional`):\n Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for\n more detail.\n return_dict (:obj:`bool`, `optional`):\n Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.\n\"\"\"\n\n\n\n@add_start_docstrings(\n \"The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.\",\n DEBERTA_START_DOCSTRING,\n)\n# Copied from transformers.models.deberta.modeling_deberta.DebertaModel with Deberta->DebertaV2\nclass DebertaV2Model(DebertaV2PreTrainedModel):\n def __init__(self, config):\n super().__init__(config)\n\n self.embeddings = DebertaV2Embeddings(config)\n self.encoder = DebertaV2Encoder(config)\n self.z_steps = 0\n self.config = config\n self.init_weights()\n\n def get_input_embeddings(self):\n return self.embeddings.word_embeddings\n\n def set_input_embeddings(self, new_embeddings):\n self.embeddings.word_embeddings = new_embeddings\n\n def _prune_heads(self, heads_to_prune):\n \"\"\"\n Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base\n class PreTrainedModel\n \"\"\"\n raise NotImplementedError(\"The prune function is not implemented in DeBERTa model.\")\n\n\n @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format(\"batch_size, sequence_length\"))\n @add_code_sample_docstrings(\n tokenizer_class=_TOKENIZER_FOR_DOC,\n checkpoint=_CHECKPOINT_FOR_DOC,\n output_type=SequenceClassifierOutput,\n config_class=_CONFIG_FOR_DOC,\n )\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n inputs_embeds=None,\n output_attentions=None,\n output_hidden_states=None,\n return_dict=None,\n ):\n output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions\n output_hidden_states = (\n output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states\n )\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n if input_ids is not None and inputs_embeds is not None:\n raise ValueError(\"You cannot specify both input_ids and inputs_embeds at the same time\")\n elif input_ids is not None:\n input_shape = input_ids.size()\n elif inputs_embeds is not None:\n input_shape = inputs_embeds.size()[:-1]\n else:\n raise ValueError(\"You have to specify either input_ids or inputs_embeds\")\n\n device = input_ids.device if input_ids is not None else inputs_embeds.device\n\n if attention_mask is None:\n attention_mask = torch.ones(input_shape, device=device)\n if token_type_ids is None:\n token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)\n\n embedding_output = self.embeddings(\n input_ids=input_ids,\n token_type_ids=token_type_ids,\n position_ids=position_ids,\n mask=attention_mask,\n inputs_embeds=inputs_embeds,\n )\n\n encoder_outputs = self.encoder(\n embedding_output,\n attention_mask,\n output_hidden_states=True,\n output_attentions=output_attentions,\n return_dict=return_dict,\n )\n encoded_layers = encoder_outputs[1]\n\n if self.z_steps > 1:\n hidden_states = encoded_layers[-2]\n layers = [self.encoder.layer[-1] for _ in range(self.z_steps)]\n query_states = encoded_layers[-1]\n rel_embeddings = self.encoder.get_rel_embedding()\n attention_mask = self.encoder.get_attention_mask(attention_mask)\n rel_pos = self.encoder.get_rel_pos(embedding_output)\n for layer in layers[1:]:\n query_states = layer(\n hidden_states,\n attention_mask,\n return_att=False,\n query_states=query_states,\n relative_pos=rel_pos,\n rel_embeddings=rel_embeddings,\n )\n encoded_layers.append(query_states)\n\n sequence_output = encoded_layers[-1]\n\n if not return_dict:\n return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :]\n\n return BaseModelOutput(\n last_hidden_state=sequence_output,\n hidden_states=encoder_outputs.hidden_states if output_hidden_states else None,\n attentions=encoder_outputs.attentions,\n )\n\n\n\n\n@add_start_docstrings(\"\"\"DeBERTa Model with a `language modeling` head on top. \"\"\", DEBERTA_START_DOCSTRING)\n# Copied from transformers.models.deberta.modeling_deberta.DebertaForMaskedLM with Deberta->DebertaV2\nclass DebertaV2ForMaskedLM(DebertaV2PreTrainedModel):\n _keys_to_ignore_on_load_unexpected = [r\"pooler\"]\n _keys_to_ignore_on_load_missing = [r\"position_ids\", r\"predictions.decoder.bias\"]\n\n def __init__(self, config):\n super().__init__(config)\n\n self.deberta = DebertaV2Model(config)\n self.cls = DebertaV2OnlyMLMHead(config)\n\n self.init_weights()\n\n def get_output_embeddings(self):\n return self.cls.predictions.decoder","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2ForMaskedLM","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2ForMaskedLM#L1107-L1182","kind":"class","name":"DebertaV2ForMaskedLM","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1107,"end_line":1182,"context_start_line":1087,"context_end_line":1202,"code":" rel_embeddings=rel_embeddings,\n )\n encoded_layers.append(query_states)\n\n sequence_output = encoded_layers[-1]\n\n if not return_dict:\n return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :]\n\n return BaseModelOutput(\n last_hidden_state=sequence_output,\n hidden_states=encoder_outputs.hidden_states if output_hidden_states else None,\n attentions=encoder_outputs.attentions,\n )\n\n\n\n\n@add_start_docstrings(\"\"\"DeBERTa Model with a `language modeling` head on top. \"\"\", DEBERTA_START_DOCSTRING)\n# Copied from transformers.models.deberta.modeling_deberta.DebertaForMaskedLM with Deberta->DebertaV2\nclass DebertaV2ForMaskedLM(DebertaV2PreTrainedModel):\n _keys_to_ignore_on_load_unexpected = [r\"pooler\"]\n _keys_to_ignore_on_load_missing = [r\"position_ids\", r\"predictions.decoder.bias\"]\n\n def __init__(self, config):\n super().__init__(config)\n\n self.deberta = DebertaV2Model(config)\n self.cls = DebertaV2OnlyMLMHead(config)\n\n self.init_weights()\n\n def get_output_embeddings(self):\n return self.cls.predictions.decoder\n\n def set_output_embeddings(self, new_embeddings):\n self.cls.predictions.decoder = new_embeddings\n\n\n @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format(\"batch_size, sequence_length\"))\n @add_code_sample_docstrings(\n tokenizer_class=_TOKENIZER_FOR_DOC,\n checkpoint=_CHECKPOINT_FOR_DOC,\n output_type=MaskedLMOutput,\n config_class=_CONFIG_FOR_DOC,\n )\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n inputs_embeds=None,\n labels=None,\n output_attentions=None,\n output_hidden_states=None,\n return_dict=None,\n ):\n r\"\"\"\n labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):\n Labels for computing the masked language modeling loss. Indices should be in ``[-100, 0, ...,\n config.vocab_size]`` (see ``input_ids`` docstring) Tokens with indices set to ``-100`` are ignored\n (masked), the loss is only computed for the tokens with labels in ``[0, ..., config.vocab_size]``\n \"\"\"\n\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n outputs = self.deberta(\n input_ids,\n attention_mask=attention_mask,\n token_type_ids=token_type_ids,\n position_ids=position_ids,\n inputs_embeds=inputs_embeds,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n sequence_output = outputs[0]\n prediction_scores = self.cls(sequence_output)\n\n masked_lm_loss = None\n if labels is not None:\n loss_fct = CrossEntropyLoss() # -100 index = padding token\n masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))\n\n if not return_dict:\n output = (prediction_scores,) + outputs[1:]\n return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output\n\n return MaskedLMOutput(\n loss=masked_lm_loss,\n logits=prediction_scores,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )\n\n\n\n# copied from transformers.models.bert.BertPredictionHeadTransform with bert -> deberta\nclass DebertaV2PredictionHeadTransform(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n if isinstance(config.hidden_act, str):\n self.transform_act_fn = ACT2FN[config.hidden_act]\n else:\n self.transform_act_fn = config.hidden_act\n self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n\n def forward(self, hidden_states):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.transform_act_fn(hidden_states)\n hidden_states = self.LayerNorm(hidden_states)\n return hidden_states\n","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2PredictionHeadTransform","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2PredictionHeadTransform#L1187-L1201","kind":"class","name":"DebertaV2PredictionHeadTransform","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1187,"end_line":1201,"context_start_line":1167,"context_end_line":1221,"code":"\n masked_lm_loss = None\n if labels is not None:\n loss_fct = CrossEntropyLoss() # -100 index = padding token\n masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))\n\n if not return_dict:\n output = (prediction_scores,) + outputs[1:]\n return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output\n\n return MaskedLMOutput(\n loss=masked_lm_loss,\n logits=prediction_scores,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )\n\n\n\n# copied from transformers.models.bert.BertPredictionHeadTransform with bert -> deberta\nclass DebertaV2PredictionHeadTransform(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n if isinstance(config.hidden_act, str):\n self.transform_act_fn = ACT2FN[config.hidden_act]\n else:\n self.transform_act_fn = config.hidden_act\n self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n\n def forward(self, hidden_states):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.transform_act_fn(hidden_states)\n hidden_states = self.LayerNorm(hidden_states)\n return hidden_states\n\n\n# copied from transformers.models.bert.BertLMPredictionHead with bert -> deberta\nclass DebertaV2LMPredictionHead(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.transform = DebertaV2PredictionHeadTransform(config)\n\n # The output weights are the same as the input embeddings, but there is\n # an output-only bias for each token.\n self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n self.decoder.bias = self.bias\n\n def forward(self, hidden_states):\n hidden_states = self.transform(hidden_states)\n hidden_states = self.decoder(hidden_states)","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2LMPredictionHead","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2LMPredictionHead#L1205-L1222","kind":"class","name":"DebertaV2LMPredictionHead","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1205,"end_line":1222,"context_start_line":1185,"context_end_line":1242,"code":"\n# copied from transformers.models.bert.BertPredictionHeadTransform with bert -> deberta\nclass DebertaV2PredictionHeadTransform(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n if isinstance(config.hidden_act, str):\n self.transform_act_fn = ACT2FN[config.hidden_act]\n else:\n self.transform_act_fn = config.hidden_act\n self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)\n\n def forward(self, hidden_states):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.transform_act_fn(hidden_states)\n hidden_states = self.LayerNorm(hidden_states)\n return hidden_states\n\n\n# copied from transformers.models.bert.BertLMPredictionHead with bert -> deberta\nclass DebertaV2LMPredictionHead(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.transform = DebertaV2PredictionHeadTransform(config)\n\n # The output weights are the same as the input embeddings, but there is\n # an output-only bias for each token.\n self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n self.decoder.bias = self.bias\n\n def forward(self, hidden_states):\n hidden_states = self.transform(hidden_states)\n hidden_states = self.decoder(hidden_states)\n return hidden_states\n\n\n# copied from transformers.models.bert.BertOnlyMLMHead with bert -> deberta\nclass DebertaV2OnlyMLMHead(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.predictions = DebertaV2LMPredictionHead(config)\n\n def forward(self, sequence_output):\n prediction_scores = self.predictions(sequence_output)\n return prediction_scores\n\n\n\n@add_start_docstrings(\n \"\"\"\n DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the\n pooled output) e.g. for GLUE tasks.\n \"\"\",\n DEBERTA_START_DOCSTRING,","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2OnlyMLMHead","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2OnlyMLMHead#L1226-L1233","kind":"class","name":"DebertaV2OnlyMLMHead","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1226,"end_line":1233,"context_start_line":1206,"context_end_line":1253,"code":" def __init__(self, config):\n super().__init__()\n self.transform = DebertaV2PredictionHeadTransform(config)\n\n # The output weights are the same as the input embeddings, but there is\n # an output-only bias for each token.\n self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n self.decoder.bias = self.bias\n\n def forward(self, hidden_states):\n hidden_states = self.transform(hidden_states)\n hidden_states = self.decoder(hidden_states)\n return hidden_states\n\n\n# copied from transformers.models.bert.BertOnlyMLMHead with bert -> deberta\nclass DebertaV2OnlyMLMHead(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.predictions = DebertaV2LMPredictionHead(config)\n\n def forward(self, sequence_output):\n prediction_scores = self.predictions(sequence_output)\n return prediction_scores\n\n\n\n@add_start_docstrings(\n \"\"\"\n DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the\n pooled output) e.g. for GLUE tasks.\n \"\"\",\n DEBERTA_START_DOCSTRING,\n)\n# Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification with Deberta->DebertaV2\nclass DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel):\n def __init__(self, config):\n super().__init__(config)\n\n num_labels = getattr(config, \"num_labels\", 2)\n self.num_labels = num_labels\n\n self.deberta = DebertaV2Model(config)\n self.pooler = ContextPooler(config)","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2ForSequenceClassification","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2ForSequenceClassification#L1245-L1342","kind":"class","name":"DebertaV2ForSequenceClassification","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1245,"end_line":1342,"context_start_line":1225,"context_end_line":1362,"code":"# copied from transformers.models.bert.BertOnlyMLMHead with bert -> deberta\nclass DebertaV2OnlyMLMHead(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.predictions = DebertaV2LMPredictionHead(config)\n\n def forward(self, sequence_output):\n prediction_scores = self.predictions(sequence_output)\n return prediction_scores\n\n\n\n@add_start_docstrings(\n \"\"\"\n DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the\n pooled output) e.g. for GLUE tasks.\n \"\"\",\n DEBERTA_START_DOCSTRING,\n)\n# Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification with Deberta->DebertaV2\nclass DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel):\n def __init__(self, config):\n super().__init__(config)\n\n num_labels = getattr(config, \"num_labels\", 2)\n self.num_labels = num_labels\n\n self.deberta = DebertaV2Model(config)\n self.pooler = ContextPooler(config)\n output_dim = self.pooler.output_dim\n\n self.classifier = nn.Linear(output_dim, num_labels)\n drop_out = getattr(config, \"cls_dropout\", None)\n drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out\n self.dropout = StableDropout(drop_out)\n\n self.init_weights()\n\n def get_input_embeddings(self):\n return self.deberta.get_input_embeddings()\n\n def set_input_embeddings(self, new_embeddings):\n self.deberta.set_input_embeddings(new_embeddings)\n\n\n @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format(\"batch_size, sequence_length\"))\n @add_code_sample_docstrings(\n tokenizer_class=_TOKENIZER_FOR_DOC,\n checkpoint=_CHECKPOINT_FOR_DOC,\n output_type=SequenceClassifierOutput,\n config_class=_CONFIG_FOR_DOC,\n )\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n inputs_embeds=None,\n labels=None,\n output_attentions=None,\n output_hidden_states=None,\n return_dict=None,\n ):\n r\"\"\"\n labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):\n Labels for computing the sequence classification/regression loss. Indices should be in :obj:`[0, ...,\n config.num_labels - 1]`. If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),\n If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).\n \"\"\"\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n outputs = self.deberta(\n input_ids,\n token_type_ids=token_type_ids,\n attention_mask=attention_mask,\n position_ids=position_ids,\n inputs_embeds=inputs_embeds,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n encoder_layer = outputs[0]\n pooled_output = self.pooler(encoder_layer)\n pooled_output = self.dropout(pooled_output)\n logits = self.classifier(pooled_output)\n\n loss = None\n if labels is not None:\n if self.num_labels == 1:\n # regression task\n loss_fn = nn.MSELoss()\n logits = logits.view(-1).to(labels.dtype)\n loss = loss_fn(logits, labels.view(-1))\n elif labels.dim() == 1 or labels.size(-1) == 1:\n label_index = (labels >= 0).nonzero()\n labels = labels.long()\n if label_index.size(0) > 0:\n labeled_logits = torch.gather(logits, 0, label_index.expand(label_index.size(0), logits.size(1)))\n labels = torch.gather(labels, 0, label_index.view(-1))\n loss_fct = CrossEntropyLoss()\n loss = loss_fct(labeled_logits.view(-1, self.num_labels).float(), labels.view(-1))\n else:\n loss = torch.tensor(0).to(logits)\n else:\n log_softmax = nn.LogSoftmax(-1)\n loss = -((log_softmax(logits) * labels).sum(-1)).mean()\n if not return_dict:\n output = (logits,) + outputs[1:]\n return ((loss,) + output) if loss is not None else output\n else:\n return SequenceClassifierOutput(\n loss=loss,\n logits=logits,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )\n\n\n\n\n@add_start_docstrings(\n \"\"\"\n DeBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for\n Named-Entity-Recognition (NER) tasks.\n \"\"\",\n DEBERTA_START_DOCSTRING,\n)\n# Copied from transformers.models.deberta.modeling_deberta.DebertaForTokenClassification with Deberta->DebertaV2\nclass DebertaV2ForTokenClassification(DebertaV2PreTrainedModel):\n _keys_to_ignore_on_load_unexpected = [r\"pooler\"]\n\n def __init__(self, config):\n super().__init__(config)\n self.num_labels = config.num_labels\n\n self.deberta = DebertaV2Model(config)","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2ForTokenClassification","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2ForTokenClassification#L1355-L1446","kind":"class","name":"DebertaV2ForTokenClassification","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1355,"end_line":1446,"context_start_line":1335,"context_end_line":1466,"code":" return ((loss,) + output) if loss is not None else output\n else:\n return SequenceClassifierOutput(\n loss=loss,\n logits=logits,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )\n\n\n\n\n@add_start_docstrings(\n \"\"\"\n DeBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for\n Named-Entity-Recognition (NER) tasks.\n \"\"\",\n DEBERTA_START_DOCSTRING,\n)\n# Copied from transformers.models.deberta.modeling_deberta.DebertaForTokenClassification with Deberta->DebertaV2\nclass DebertaV2ForTokenClassification(DebertaV2PreTrainedModel):\n _keys_to_ignore_on_load_unexpected = [r\"pooler\"]\n\n def __init__(self, config):\n super().__init__(config)\n self.num_labels = config.num_labels\n\n self.deberta = DebertaV2Model(config)\n self.dropout = nn.Dropout(config.hidden_dropout_prob)\n self.classifier = nn.Linear(config.hidden_size, config.num_labels)\n\n self.init_weights()\n\n\n @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format(\"batch_size, sequence_length\"))\n @add_code_sample_docstrings(\n tokenizer_class=_TOKENIZER_FOR_DOC,\n checkpoint=_CHECKPOINT_FOR_DOC,\n output_type=TokenClassifierOutput,\n config_class=_CONFIG_FOR_DOC,\n )\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n inputs_embeds=None,\n labels=None,\n output_attentions=None,\n output_hidden_states=None,\n return_dict=None,\n ):\n r\"\"\"\n labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):\n Labels for computing the token classification loss. Indices should be in ``[0, ..., config.num_labels -\n 1]``.\n \"\"\"\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n outputs = self.deberta(\n input_ids,\n attention_mask=attention_mask,\n token_type_ids=token_type_ids,\n position_ids=position_ids,\n inputs_embeds=inputs_embeds,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n sequence_output = outputs[0]\n\n sequence_output = self.dropout(sequence_output)\n logits = self.classifier(sequence_output)\n\n loss = None\n if labels is not None:\n # pdb.set_trace()\n\n # code change begin\n loss_weights = torch.zeros(len(self.config.label2id)).to(self.device)\n loss_weights[self.config.label2id[\"SOLUTION-CORRECT\"]] = self.config.task_specific_params[\"solution_correct_loss_weight\"]\n loss_weights[self.config.label2id[\"SOLUTION-INCORRECT\"]] = self.config.task_specific_params[\"solution_incorrect_loss_weight\"]\n loss_weights[self.config.label2id[\"STEP-CORRECT\"]] = self.config.task_specific_params[\"step_correct_loss_weight\"]\n loss_weights[self.config.label2id[\"STEP-INCORRECT\"]] = self.config.task_specific_params[\"step_incorrect_loss_weight\"]\n loss_weights[self.config.label2id[\"O\"]] = self.config.task_specific_params[\"other_label_loss_weight\"]\n # code change end\n\n loss_fct = CrossEntropyLoss(weight=loss_weights)\n # pdb.set_trace()\n # Only keep active parts of the loss\n if attention_mask is not None:\n active_loss = attention_mask.view(-1) == 1\n active_logits = logits.view(-1, self.num_labels)\n active_labels = torch.where(\n active_loss, labels.view(-1), torch.tensor(loss_fct.ignore_index).type_as(labels)\n )\n loss = loss_fct(active_logits, active_labels)\n else:\n loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n\n if not return_dict:\n output = (logits,) + outputs[1:]\n return ((loss,) + output) if loss is not None else output\n\n return TokenClassifierOutput(\n loss=loss,\n logits=logits,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )\n\n\n\n\n@add_start_docstrings(\n \"\"\"\n DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear\n layers on top of the hidden-states output to compute `span start logits` and `span end logits`).\n \"\"\",\n DEBERTA_START_DOCSTRING,\n)\n# Copied from transformers.models.deberta.modeling_deberta.DebertaForQuestionAnswering with Deberta->DebertaV2\nclass DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):\n _keys_to_ignore_on_load_unexpected = [r\"pooler\"]\n\n def __init__(self, config):\n super().__init__(config)\n self.num_labels = config.num_labels\n\n self.deberta = DebertaV2Model(config)","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.DebertaV2ForQuestionAnswering","uri":"program://CodeT/class/DIVERSE.code.src.deberta_model.DebertaV2ForQuestionAnswering#L1459-L1549","kind":"class","name":"DebertaV2ForQuestionAnswering","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1459,"end_line":1549,"context_start_line":1439,"context_end_line":1549,"code":" return ((loss,) + output) if loss is not None else output\n\n return TokenClassifierOutput(\n loss=loss,\n logits=logits,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )\n\n\n\n\n@add_start_docstrings(\n \"\"\"\n DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear\n layers on top of the hidden-states output to compute `span start logits` and `span end logits`).\n \"\"\",\n DEBERTA_START_DOCSTRING,\n)\n# Copied from transformers.models.deberta.modeling_deberta.DebertaForQuestionAnswering with Deberta->DebertaV2\nclass DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):\n _keys_to_ignore_on_load_unexpected = [r\"pooler\"]\n\n def __init__(self, config):\n super().__init__(config)\n self.num_labels = config.num_labels\n\n self.deberta = DebertaV2Model(config)\n self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)\n\n self.init_weights()\n\n\n @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format(\"batch_size, sequence_length\"))\n @add_code_sample_docstrings(\n tokenizer_class=_TOKENIZER_FOR_DOC,\n checkpoint=_CHECKPOINT_FOR_DOC,\n output_type=QuestionAnsweringModelOutput,\n config_class=_CONFIG_FOR_DOC,\n )\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n inputs_embeds=None,\n start_positions=None,\n end_positions=None,\n output_attentions=None,\n output_hidden_states=None,\n return_dict=None,\n ):\n r\"\"\"\n start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):\n Labels for position (index) of the start of the labelled span for computing the token classification loss.\n Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the\n sequence are not taken into account for computing the loss.\n end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):\n Labels for position (index) of the end of the labelled span for computing the token classification loss.\n Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the\n sequence are not taken into account for computing the loss.\n \"\"\"\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n outputs = self.deberta(\n input_ids,\n attention_mask=attention_mask,\n token_type_ids=token_type_ids,\n position_ids=position_ids,\n inputs_embeds=inputs_embeds,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n sequence_output = outputs[0]\n\n logits = self.qa_outputs(sequence_output)\n start_logits, end_logits = logits.split(1, dim=-1)\n start_logits = start_logits.squeeze(-1).contiguous()\n end_logits = end_logits.squeeze(-1).contiguous()\n\n total_loss = None\n if start_positions is not None and end_positions is not None:\n # If we are on multi-GPU, split add a dimension\n if len(start_positions.size()) > 1:\n start_positions = start_positions.squeeze(-1)\n if len(end_positions.size()) > 1:\n end_positions = end_positions.squeeze(-1)\n # sometimes the start/end positions are outside our model inputs, we ignore these terms\n ignored_index = start_logits.size(1)\n start_positions = start_positions.clamp(0, ignored_index)\n end_positions = end_positions.clamp(0, ignored_index)\n\n loss_fct = CrossEntropyLoss(ignore_index=ignored_index)\n start_loss = loss_fct(start_logits, start_positions)\n end_loss = loss_fct(end_logits, end_positions)\n total_loss = (start_loss + end_loss) / 2\n\n if not return_dict:\n output = (start_logits, end_logits) + outputs[1:]\n return ((total_loss,) + output) if total_loss is not None else output\n\n return QuestionAnsweringModelOutput(\n loss=total_loss,\n start_logits=start_logits,\n end_logits=end_logits,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.__init__","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.__init__#L1462-L1469","kind":"function","name":"__init__","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1462,"end_line":1469,"context_start_line":1442,"context_end_line":1489,"code":" loss=loss,\n logits=logits,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )\n\n\n\n\n@add_start_docstrings(\n \"\"\"\n DeBERTa Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear\n layers on top of the hidden-states output to compute `span start logits` and `span end logits`).\n \"\"\",\n DEBERTA_START_DOCSTRING,\n)\n# Copied from transformers.models.deberta.modeling_deberta.DebertaForQuestionAnswering with Deberta->DebertaV2\nclass DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):\n _keys_to_ignore_on_load_unexpected = [r\"pooler\"]\n\n def __init__(self, config):\n super().__init__(config)\n self.num_labels = config.num_labels\n\n self.deberta = DebertaV2Model(config)\n self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)\n\n self.init_weights()\n\n\n @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format(\"batch_size, sequence_length\"))\n @add_code_sample_docstrings(\n tokenizer_class=_TOKENIZER_FOR_DOC,\n checkpoint=_CHECKPOINT_FOR_DOC,\n output_type=QuestionAnsweringModelOutput,\n config_class=_CONFIG_FOR_DOC,\n )\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n inputs_embeds=None,\n start_positions=None,\n end_positions=None,\n output_attentions=None,\n output_hidden_states=None,","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.forward","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.forward#L1479-L1549","kind":"function","name":"forward","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1479,"end_line":1549,"context_start_line":1459,"context_end_line":1549,"code":"class DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):\n _keys_to_ignore_on_load_unexpected = [r\"pooler\"]\n\n def __init__(self, config):\n super().__init__(config)\n self.num_labels = config.num_labels\n\n self.deberta = DebertaV2Model(config)\n self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)\n\n self.init_weights()\n\n\n @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format(\"batch_size, sequence_length\"))\n @add_code_sample_docstrings(\n tokenizer_class=_TOKENIZER_FOR_DOC,\n checkpoint=_CHECKPOINT_FOR_DOC,\n output_type=QuestionAnsweringModelOutput,\n config_class=_CONFIG_FOR_DOC,\n )\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n inputs_embeds=None,\n start_positions=None,\n end_positions=None,\n output_attentions=None,\n output_hidden_states=None,\n return_dict=None,\n ):\n r\"\"\"\n start_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):\n Labels for position (index) of the start of the labelled span for computing the token classification loss.\n Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the\n sequence are not taken into account for computing the loss.\n end_positions (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):\n Labels for position (index) of the end of the labelled span for computing the token classification loss.\n Positions are clamped to the length of the sequence (:obj:`sequence_length`). Position outside of the\n sequence are not taken into account for computing the loss.\n \"\"\"\n return_dict = return_dict if return_dict is not None else self.config.use_return_dict\n\n outputs = self.deberta(\n input_ids,\n attention_mask=attention_mask,\n token_type_ids=token_type_ids,\n position_ids=position_ids,\n inputs_embeds=inputs_embeds,\n output_attentions=output_attentions,\n output_hidden_states=output_hidden_states,\n return_dict=return_dict,\n )\n\n sequence_output = outputs[0]\n\n logits = self.qa_outputs(sequence_output)\n start_logits, end_logits = logits.split(1, dim=-1)\n start_logits = start_logits.squeeze(-1).contiguous()\n end_logits = end_logits.squeeze(-1).contiguous()\n\n total_loss = None\n if start_positions is not None and end_positions is not None:\n # If we are on multi-GPU, split add a dimension\n if len(start_positions.size()) > 1:\n start_positions = start_positions.squeeze(-1)\n if len(end_positions.size()) > 1:\n end_positions = end_positions.squeeze(-1)\n # sometimes the start/end positions are outside our model inputs, we ignore these terms\n ignored_index = start_logits.size(1)\n start_positions = start_positions.clamp(0, ignored_index)\n end_positions = end_positions.clamp(0, ignored_index)\n\n loss_fct = CrossEntropyLoss(ignore_index=ignored_index)\n start_loss = loss_fct(start_logits, start_positions)\n end_loss = loss_fct(end_logits, end_positions)\n total_loss = (start_loss + end_loss) / 2\n\n if not return_dict:\n output = (start_logits, end_logits) + outputs[1:]\n return ((total_loss,) + output) if total_loss is not None else output\n\n return QuestionAnsweringModelOutput(\n loss=total_loss,\n start_logits=start_logits,\n end_logits=end_logits,\n hidden_states=outputs.hidden_states,\n attentions=outputs.attentions,\n )","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.output_dim","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.output_dim#L72-L73","kind":"function","name":"output_dim","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":72,"end_line":73,"context_start_line":52,"context_end_line":93,"code":"\n# Copied from transformers.models.deberta.modeling_deberta.ContextPooler\nclass ContextPooler(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.pooler_hidden_size, config.pooler_hidden_size)\n self.dropout = StableDropout(config.pooler_dropout)\n self.config = config\n\n def forward(self, hidden_states):\n # We \"pool\" the model by simply taking the hidden state corresponding\n # to the first token.\n\n context_token = hidden_states[:, 0]\n context_token = self.dropout(context_token)\n pooled_output = self.dense(context_token)\n pooled_output = ACT2FN[self.config.pooler_hidden_act](pooled_output)\n return pooled_output\n\n @property\n def output_dim(self):\n return self.config.hidden_size\n\n\n# Copied from transformers.models.deberta.modeling_deberta.XSoftmax with deberta->deberta_v2\nclass XSoftmax(torch.autograd.Function):\n \"\"\"\n Masked Softmax which is optimized for saving memory\n\n Args:\n input (:obj:`torch.tensor`): The input tensor that will apply softmax.\n mask (:obj:`torch.IntTensor`): The mask matrix where 0 indicate that element will be ignored in the softmax calculation.\n dim (int): The dimension that will apply softmax\n\n Example::\n\n >>> import torch\n >>> from transformers.models.deberta_v2.modeling_deberta_v2 import XSoftmax\n\n >>> # Make a tensor\n >>> x = torch.randn([4,20,100])\n","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.backward","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.backward#L162-L167","kind":"function","name":"backward","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":162,"end_line":167,"context_start_line":142,"context_end_line":187,"code":" local_context.mask = mask\n\n return mask, dropout\n\n\n# Copied from transformers.models.deberta.modeling_deberta.XDropout\nclass XDropout(torch.autograd.Function):\n \"\"\"Optimized dropout function to save computation and memory by using mask operation instead of multiplication.\"\"\"\n\n @staticmethod\n def forward(ctx, input, local_ctx):\n mask, dropout = get_mask(input, local_ctx)\n ctx.scale = 1.0 / (1 - dropout)\n if dropout > 0:\n ctx.save_for_backward(mask)\n return input.masked_fill(mask, 0) * ctx.scale\n else:\n return input\n\n @staticmethod\n def backward(ctx, grad_output):\n if ctx.scale > 1:\n (mask,) = ctx.saved_tensors\n return grad_output.masked_fill(mask, 0) * ctx.scale, None\n else:\n return grad_output, None\n\n\n# Copied from transformers.models.deberta.modeling_deberta.StableDropout\nclass StableDropout(nn.Module):\n \"\"\"\n Optimized dropout module for stabilizing the training\n\n Args:\n drop_prob (float): the dropout probabilities\n \"\"\"\n\n def __init__(self, drop_prob):\n super().__init__()\n self.drop_prob = drop_prob\n self.count = 0\n self.context_stack = None\n\n def forward(self, x):\n \"\"\"\n Call the module","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.clear_context","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.clear_context#L196-L198","kind":"function","name":"clear_context","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":196,"end_line":198,"context_start_line":176,"context_end_line":218,"code":" drop_prob (float): the dropout probabilities\n \"\"\"\n\n def __init__(self, drop_prob):\n super().__init__()\n self.drop_prob = drop_prob\n self.count = 0\n self.context_stack = None\n\n def forward(self, x):\n \"\"\"\n Call the module\n\n Args:\n x (:obj:`torch.tensor`): The input tensor to apply dropout\n \"\"\"\n if self.training and self.drop_prob > 0:\n return XDropout.apply(x, self.get_context())\n return x\n\n def clear_context(self):\n self.count = 0\n self.context_stack = None\n\n def init_context(self, reuse_mask=True, scale=1):\n if self.context_stack is None:\n self.context_stack = []\n self.count = 0\n for c in self.context_stack:\n c.reuse_mask = reuse_mask\n c.scale = scale\n\n def get_context(self):\n if self.context_stack is not None:\n if self.count >= len(self.context_stack):\n self.context_stack.append(DropoutContext())\n ctx = self.context_stack[self.count]\n ctx.dropout = self.drop_prob\n self.count += 1\n return ctx\n else:\n return self.drop_prob\n","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.init_context","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.init_context#L200-L206","kind":"function","name":"init_context","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":200,"end_line":206,"context_start_line":180,"context_end_line":226,"code":" super().__init__()\n self.drop_prob = drop_prob\n self.count = 0\n self.context_stack = None\n\n def forward(self, x):\n \"\"\"\n Call the module\n\n Args:\n x (:obj:`torch.tensor`): The input tensor to apply dropout\n \"\"\"\n if self.training and self.drop_prob > 0:\n return XDropout.apply(x, self.get_context())\n return x\n\n def clear_context(self):\n self.count = 0\n self.context_stack = None\n\n def init_context(self, reuse_mask=True, scale=1):\n if self.context_stack is None:\n self.context_stack = []\n self.count = 0\n for c in self.context_stack:\n c.reuse_mask = reuse_mask\n c.scale = scale\n\n def get_context(self):\n if self.context_stack is not None:\n if self.count >= len(self.context_stack):\n self.context_stack.append(DropoutContext())\n ctx = self.context_stack[self.count]\n ctx.dropout = self.drop_prob\n self.count += 1\n return ctx\n else:\n return self.drop_prob\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm\nclass DebertaV2SelfOutput(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)\n self.dropout = StableDropout(config.hidden_dropout_prob)","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.get_context","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.get_context#L208-L217","kind":"function","name":"get_context","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":208,"end_line":217,"context_start_line":188,"context_end_line":237,"code":"\n Args:\n x (:obj:`torch.tensor`): The input tensor to apply dropout\n \"\"\"\n if self.training and self.drop_prob > 0:\n return XDropout.apply(x, self.get_context())\n return x\n\n def clear_context(self):\n self.count = 0\n self.context_stack = None\n\n def init_context(self, reuse_mask=True, scale=1):\n if self.context_stack is None:\n self.context_stack = []\n self.count = 0\n for c in self.context_stack:\n c.reuse_mask = reuse_mask\n c.scale = scale\n\n def get_context(self):\n if self.context_stack is not None:\n if self.count >= len(self.context_stack):\n self.context_stack.append(DropoutContext())\n ctx = self.context_stack[self.count]\n ctx.dropout = self.drop_prob\n self.count += 1\n return ctx\n else:\n return self.drop_prob\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaSelfOutput with DebertaLayerNorm->LayerNorm\nclass DebertaV2SelfOutput(nn.Module):\n def __init__(self, config):\n super().__init__()\n self.dense = nn.Linear(config.hidden_size, config.hidden_size)\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps)\n self.dropout = StableDropout(config.hidden_dropout_prob)\n\n def forward(self, hidden_states, input_tensor):\n hidden_states = self.dense(hidden_states)\n hidden_states = self.dropout(hidden_states)\n hidden_states = self.LayerNorm(hidden_states + input_tensor)\n return hidden_states\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaAttention with Deberta->DebertaV2\nclass DebertaV2Attention(nn.Module):\n def __init__(self, config):","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.get_rel_embedding","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.get_rel_embedding#L404-L408","kind":"function","name":"get_rel_embedding","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":404,"end_line":408,"context_start_line":384,"context_end_line":428,"code":" if self.relative_attention:\n self.max_relative_positions = getattr(config, \"max_relative_positions\", -1)\n if self.max_relative_positions < 1:\n self.max_relative_positions = config.max_position_embeddings\n\n self.position_buckets = getattr(config, \"position_buckets\", -1)\n pos_ebd_size = self.max_relative_positions * 2\n\n if self.position_buckets > 0:\n pos_ebd_size = self.position_buckets * 2\n\n self.rel_embeddings = nn.Embedding(pos_ebd_size, config.hidden_size)\n\n self.norm_rel_ebd = [x.strip() for x in getattr(config, \"norm_rel_ebd\", \"none\").lower().split(\"|\")]\n\n if \"layer_norm\" in self.norm_rel_ebd:\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True)\n\n self.conv = ConvLayer(config) if getattr(config, \"conv_kernel_size\", 0) > 0 else None\n\n def get_rel_embedding(self):\n rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None\n if rel_embeddings is not None and (\"layer_norm\" in self.norm_rel_ebd):\n rel_embeddings = self.LayerNorm(rel_embeddings)\n return rel_embeddings\n\n def get_attention_mask(self, attention_mask):\n if attention_mask.dim() <= 2:\n extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)\n attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1)\n attention_mask = attention_mask.byte()\n elif attention_mask.dim() == 3:\n attention_mask = attention_mask.unsqueeze(1)\n\n return attention_mask\n\n def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None):\n if self.relative_attention and relative_pos is None:\n q = query_states.size(-2) if query_states is not None else hidden_states.size(-2)\n relative_pos = build_relative_position(\n q, hidden_states.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions\n )\n return relative_pos\n\n def forward(","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.get_attention_mask","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.get_attention_mask#L410-L418","kind":"function","name":"get_attention_mask","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":410,"end_line":418,"context_start_line":390,"context_end_line":438,"code":" pos_ebd_size = self.max_relative_positions * 2\n\n if self.position_buckets > 0:\n pos_ebd_size = self.position_buckets * 2\n\n self.rel_embeddings = nn.Embedding(pos_ebd_size, config.hidden_size)\n\n self.norm_rel_ebd = [x.strip() for x in getattr(config, \"norm_rel_ebd\", \"none\").lower().split(\"|\")]\n\n if \"layer_norm\" in self.norm_rel_ebd:\n self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True)\n\n self.conv = ConvLayer(config) if getattr(config, \"conv_kernel_size\", 0) > 0 else None\n\n def get_rel_embedding(self):\n rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None\n if rel_embeddings is not None and (\"layer_norm\" in self.norm_rel_ebd):\n rel_embeddings = self.LayerNorm(rel_embeddings)\n return rel_embeddings\n\n def get_attention_mask(self, attention_mask):\n if attention_mask.dim() <= 2:\n extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)\n attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1)\n attention_mask = attention_mask.byte()\n elif attention_mask.dim() == 3:\n attention_mask = attention_mask.unsqueeze(1)\n\n return attention_mask\n\n def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None):\n if self.relative_attention and relative_pos is None:\n q = query_states.size(-2) if query_states is not None else hidden_states.size(-2)\n relative_pos = build_relative_position(\n q, hidden_states.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions\n )\n return relative_pos\n\n def forward(\n self,\n hidden_states,\n attention_mask,\n output_hidden_states=True,\n output_attentions=False,\n query_states=None,\n relative_pos=None,\n return_dict=True,\n ):\n if attention_mask.dim() <= 2:","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.get_rel_pos","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.get_rel_pos#L420-L426","kind":"function","name":"get_rel_pos","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":420,"end_line":426,"context_start_line":400,"context_end_line":446,"code":" self.LayerNorm = LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True)\n\n self.conv = ConvLayer(config) if getattr(config, \"conv_kernel_size\", 0) > 0 else None\n\n def get_rel_embedding(self):\n rel_embeddings = self.rel_embeddings.weight if self.relative_attention else None\n if rel_embeddings is not None and (\"layer_norm\" in self.norm_rel_ebd):\n rel_embeddings = self.LayerNorm(rel_embeddings)\n return rel_embeddings\n\n def get_attention_mask(self, attention_mask):\n if attention_mask.dim() <= 2:\n extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)\n attention_mask = extended_attention_mask * extended_attention_mask.squeeze(-2).unsqueeze(-1)\n attention_mask = attention_mask.byte()\n elif attention_mask.dim() == 3:\n attention_mask = attention_mask.unsqueeze(1)\n\n return attention_mask\n\n def get_rel_pos(self, hidden_states, query_states=None, relative_pos=None):\n if self.relative_attention and relative_pos is None:\n q = query_states.size(-2) if query_states is not None else hidden_states.size(-2)\n relative_pos = build_relative_position(\n q, hidden_states.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions\n )\n return relative_pos\n\n def forward(\n self,\n hidden_states,\n attention_mask,\n output_hidden_states=True,\n output_attentions=False,\n query_states=None,\n relative_pos=None,\n return_dict=True,\n ):\n if attention_mask.dim() <= 2:\n input_mask = attention_mask\n else:\n input_mask = (attention_mask.sum(-2) > 0).byte()\n attention_mask = self.get_attention_mask(attention_mask)\n relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos)\n\n all_hidden_states = () if output_hidden_states else None\n all_attentions = () if output_attentions else None","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.transpose_for_scores","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.transpose_for_scores#L598-L601","kind":"function","name":"transpose_for_scores","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":598,"end_line":601,"context_start_line":578,"context_end_line":621,"code":"\n if self.relative_attention:\n self.position_buckets = getattr(config, \"position_buckets\", -1)\n self.max_relative_positions = getattr(config, \"max_relative_positions\", -1)\n if self.max_relative_positions < 1:\n self.max_relative_positions = config.max_position_embeddings\n self.pos_ebd_size = self.max_relative_positions\n if self.position_buckets > 0:\n self.pos_ebd_size = self.position_buckets\n\n self.pos_dropout = StableDropout(config.hidden_dropout_prob)\n\n if not self.share_att_key:\n if \"c2p\" in self.pos_att_type or \"p2p\" in self.pos_att_type:\n self.pos_key_proj = nn.Linear(config.hidden_size, self.all_head_size, bias=True)\n if \"p2c\" in self.pos_att_type or \"p2p\" in self.pos_att_type:\n self.pos_query_proj = nn.Linear(config.hidden_size, self.all_head_size)\n\n self.dropout = StableDropout(config.attention_probs_dropout_prob)\n\n def transpose_for_scores(self, x, attention_heads):\n new_x_shape = x.size()[:-1] + (attention_heads, -1)\n x = x.view(*new_x_shape)\n return x.permute(0, 2, 1, 3).contiguous().view(-1, x.size(1), x.size(-1))\n\n def forward(\n self,\n hidden_states,\n attention_mask,\n return_att=False,\n query_states=None,\n relative_pos=None,\n rel_embeddings=None,\n ):\n \"\"\"\n Call the module\n\n Args:\n hidden_states (:obj:`torch.FloatTensor`):\n Input states to the module usually the output from previous layer, it will be the Q,K and V in\n `Attention(Q,K,V)`\n\n attention_mask (:obj:`torch.ByteTensor`):\n An attention mask matrix of shape [`B`, `N`, `N`] where `B` is the batch size, `N` is the maximum","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.disentangled_attention_bias","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.disentangled_attention_bias#L689-L794","kind":"function","name":"disentangled_attention_bias","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":689,"end_line":794,"context_start_line":669,"context_end_line":814,"code":" )\n\n # bsz x height x length x dimension\n attention_probs = XSoftmax.apply(attention_scores, attention_mask, -1)\n attention_probs = self.dropout(attention_probs)\n context_layer = torch.bmm(\n attention_probs.view(-1, attention_probs.size(-2), attention_probs.size(-1)), value_layer\n )\n context_layer = (\n context_layer.view(-1, self.num_attention_heads, context_layer.size(-2), context_layer.size(-1))\n .permute(0, 2, 1, 3)\n .contiguous()\n )\n new_context_layer_shape = context_layer.size()[:-2] + (-1,)\n context_layer = context_layer.view(*new_context_layer_shape)\n if return_att:\n return (context_layer, attention_probs)\n else:\n return context_layer\n\n def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor):\n if relative_pos is None:\n q = query_layer.size(-2)\n relative_pos = build_relative_position(\n q, key_layer.size(-2), bucket_size=self.position_buckets, max_position=self.max_relative_positions\n )\n if relative_pos.dim() == 2:\n relative_pos = relative_pos.unsqueeze(0).unsqueeze(0)\n elif relative_pos.dim() == 3:\n relative_pos = relative_pos.unsqueeze(1)\n # bsz x height x query x key\n elif relative_pos.dim() != 4:\n raise ValueError(f\"Relative position ids must be of dim 2 or 3 or 4. {relative_pos.dim()}\")\n\n att_span = self.pos_ebd_size\n relative_pos = relative_pos.long().to(query_layer.device)\n\n rel_embeddings = rel_embeddings[self.pos_ebd_size - att_span : self.pos_ebd_size + att_span, :].unsqueeze(0)\n if self.share_att_key:\n pos_query_layer = self.transpose_for_scores(\n self.query_proj(rel_embeddings), self.num_attention_heads\n ).repeat(query_layer.size(0) // self.num_attention_heads, 1, 1)\n pos_key_layer = self.transpose_for_scores(self.key_proj(rel_embeddings), self.num_attention_heads).repeat(\n query_layer.size(0) // self.num_attention_heads, 1, 1\n )\n else:\n if \"c2p\" in self.pos_att_type or \"p2p\" in self.pos_att_type:\n pos_key_layer = self.transpose_for_scores(\n self.pos_key_proj(rel_embeddings), self.num_attention_heads\n ).repeat(\n query_layer.size(0) // self.num_attention_heads, 1, 1\n ) # .split(self.all_head_size, dim=-1)\n if \"p2c\" in self.pos_att_type or \"p2p\" in self.pos_att_type:\n pos_query_layer = self.transpose_for_scores(\n self.pos_query_proj(rel_embeddings), self.num_attention_heads\n ).repeat(\n query_layer.size(0) // self.num_attention_heads, 1, 1\n ) # .split(self.all_head_size, dim=-1)\n\n score = 0\n # content->position\n if \"c2p\" in self.pos_att_type:\n scale = math.sqrt(pos_key_layer.size(-1) * scale_factor)\n c2p_att = torch.bmm(query_layer, pos_key_layer.transpose(-1, -2))\n c2p_pos = torch.clamp(relative_pos + att_span, 0, att_span * 2 - 1)\n c2p_att = torch.gather(\n c2p_att,\n dim=-1,\n index=c2p_pos.squeeze(0).expand([query_layer.size(0), query_layer.size(1), relative_pos.size(-1)]),\n )\n score += c2p_att / scale\n\n # position->content\n if \"p2c\" in self.pos_att_type or \"p2p\" in self.pos_att_type:\n scale = math.sqrt(pos_query_layer.size(-1) * scale_factor)\n if key_layer.size(-2) != query_layer.size(-2):\n r_pos = build_relative_position(\n key_layer.size(-2),\n key_layer.size(-2),\n bucket_size=self.position_buckets,\n max_position=self.max_relative_positions,\n ).to(query_layer.device)\n r_pos = r_pos.unsqueeze(0)\n else:\n r_pos = relative_pos\n\n p2c_pos = torch.clamp(-r_pos + att_span, 0, att_span * 2 - 1)\n if query_layer.size(-2) != key_layer.size(-2):\n pos_index = relative_pos[:, :, :, 0].unsqueeze(-1)\n\n if \"p2c\" in self.pos_att_type:\n p2c_att = torch.bmm(key_layer, pos_query_layer.transpose(-1, -2))\n p2c_att = torch.gather(\n p2c_att,\n dim=-1,\n index=p2c_pos.squeeze(0).expand([query_layer.size(0), key_layer.size(-2), key_layer.size(-2)]),\n ).transpose(-1, -2)\n if query_layer.size(-2) != key_layer.size(-2):\n p2c_att = torch.gather(\n p2c_att,\n dim=-2,\n index=pos_index.expand(p2c_att.size()[:2] + (pos_index.size(-2), key_layer.size(-2))),\n )\n score += p2c_att / scale\n\n # position->position\n if \"p2p\" in self.pos_att_type:\n pos_query = pos_query_layer[:, :, att_span:, :]\n p2p_att = torch.matmul(pos_query, pos_key_layer.transpose(-1, -2))\n p2p_att = p2p_att.expand(query_layer.size()[:2] + p2p_att.size()[2:])\n if query_layer.size(-2) != key_layer.size(-2):\n p2p_att = torch.gather(\n p2p_att,\n dim=-2,\n index=pos_index.expand(query_layer.size()[:2] + (pos_index.size(-2), p2p_att.size(-1))),\n )\n p2p_att = torch.gather(\n p2p_att,\n dim=-1,\n index=c2p_pos.expand(\n [query_layer.size(0), query_layer.size(1), query_layer.size(2), relative_pos.size(-1)]\n ),\n )\n score += p2p_att\n\n return score\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaEmbeddings with DebertaLayerNorm->LayerNorm\nclass DebertaV2Embeddings(nn.Module):\n \"\"\"Construct the embeddings from word, position and token_type embeddings.\"\"\"\n\n def __init__(self, config):\n super().__init__()\n pad_token_id = getattr(config, \"pad_token_id\", 0)\n self.embedding_size = getattr(config, \"embedding_size\", config.hidden_size)\n self.word_embeddings = nn.Embedding(config.vocab_size, self.embedding_size, padding_idx=pad_token_id)\n\n self.position_biased_input = getattr(config, \"position_biased_input\", True)\n if not self.position_biased_input:\n self.position_embeddings = None\n else:\n self.position_embeddings = nn.Embedding(config.max_position_embeddings, self.embedding_size)\n\n if config.type_vocab_size > 0:\n self.token_type_embeddings = nn.Embedding(config.type_vocab_size, self.embedding_size)","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model._init_weights","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model._init_weights#L889-L900","kind":"function","name":"_init_weights","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":889,"end_line":900,"context_start_line":869,"context_end_line":920,"code":" return embeddings\n\n\n# Copied from transformers.models.deberta.modeling_deberta.DebertaPreTrainedModel with Deberta->DebertaV2\n\nclass DebertaV2PreTrainedModel(PreTrainedModel):\n \"\"\"\n An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained\n models.\n \"\"\"\n\n config_class = DebertaV2Config\n base_model_prefix = \"deberta\"\n _keys_to_ignore_on_load_missing = [\"position_ids\"]\n _keys_to_ignore_on_load_unexpected = [\"position_embeddings\"]\n\n def __init__(self, config):\n super().__init__(config)\n self._register_load_state_dict_pre_hook(self._pre_load_hook)\n\n def _init_weights(self, module):\n \"\"\"Initialize the weights.\"\"\"\n if isinstance(module, nn.Linear):\n # Slightly different from the TF version which uses truncated_normal for initialization\n # cf https://github.com/pytorch/pytorch/pull/5617\n module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n if module.bias is not None:\n module.bias.data.zero_()\n elif isinstance(module, nn.Embedding):\n module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n if module.padding_idx is not None:\n module.weight.data[module.padding_idx].zero_()\n\n def _pre_load_hook(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):\n \"\"\"\n Removes the classifier if it doesn't have the correct number of labels.\n \"\"\"\n self_state = self.state_dict()\n if (\n (\"classifier.weight\" in self_state)\n and (\"classifier.weight\" in state_dict)\n and self_state[\"classifier.weight\"].size() != state_dict[\"classifier.weight\"].size()\n ):\n logger.warning(\n f\"The checkpoint classifier head has a shape {state_dict['classifier.weight'].size()} and this model \"\n f\"classifier head has a shape {self_state['classifier.weight'].size()}. Ignoring the checkpoint \"\n f\"weights. You should train your model on new data.\"\n )\n del state_dict[\"classifier.weight\"]\n if \"classifier.bias\" in state_dict:\n del state_dict[\"classifier.bias\"]\n","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model._pre_load_hook","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model._pre_load_hook#L902-L919","kind":"function","name":"_pre_load_hook","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":902,"end_line":919,"context_start_line":882,"context_end_line":939,"code":" _keys_to_ignore_on_load_missing = [\"position_ids\"]\n _keys_to_ignore_on_load_unexpected = [\"position_embeddings\"]\n\n def __init__(self, config):\n super().__init__(config)\n self._register_load_state_dict_pre_hook(self._pre_load_hook)\n\n def _init_weights(self, module):\n \"\"\"Initialize the weights.\"\"\"\n if isinstance(module, nn.Linear):\n # Slightly different from the TF version which uses truncated_normal for initialization\n # cf https://github.com/pytorch/pytorch/pull/5617\n module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n if module.bias is not None:\n module.bias.data.zero_()\n elif isinstance(module, nn.Embedding):\n module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)\n if module.padding_idx is not None:\n module.weight.data[module.padding_idx].zero_()\n\n def _pre_load_hook(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):\n \"\"\"\n Removes the classifier if it doesn't have the correct number of labels.\n \"\"\"\n self_state = self.state_dict()\n if (\n (\"classifier.weight\" in self_state)\n and (\"classifier.weight\" in state_dict)\n and self_state[\"classifier.weight\"].size() != state_dict[\"classifier.weight\"].size()\n ):\n logger.warning(\n f\"The checkpoint classifier head has a shape {state_dict['classifier.weight'].size()} and this model \"\n f\"classifier head has a shape {self_state['classifier.weight'].size()}. Ignoring the checkpoint \"\n f\"weights. You should train your model on new data.\"\n )\n del state_dict[\"classifier.weight\"]\n if \"classifier.bias\" in state_dict:\n del state_dict[\"classifier.bias\"]\n\n\n\nDEBERTA_START_DOCSTRING = r\"\"\"\n The DeBERTa model was proposed in `DeBERTa: Decoding-enhanced BERT with Disentangled Attention\n `_ by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It's build on top of\n BERT/RoBERTa with two improvements, i.e. disentangled attention and enhanced mask decoder. With those two\n improvements, it out perform BERT/RoBERTa on a majority of tasks with 80GB pretraining data.\n\n This model is also a PyTorch `torch.nn.Module `__\n subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to\n general usage and behavior.```\n\n\n Parameters:\n config (:class:`~transformers.DebertaV2Config`): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model\n weights.\n\"\"\"","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.get_input_embeddings","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.get_input_embeddings#L1263-L1264","kind":"function","name":"get_input_embeddings","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1263,"end_line":1264,"context_start_line":1243,"context_end_line":1284,"code":")\n# Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification with Deberta->DebertaV2\nclass DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel):\n def __init__(self, config):\n super().__init__(config)\n\n num_labels = getattr(config, \"num_labels\", 2)\n self.num_labels = num_labels\n\n self.deberta = DebertaV2Model(config)\n self.pooler = ContextPooler(config)\n output_dim = self.pooler.output_dim\n\n self.classifier = nn.Linear(output_dim, num_labels)\n drop_out = getattr(config, \"cls_dropout\", None)\n drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out\n self.dropout = StableDropout(drop_out)\n\n self.init_weights()\n\n def get_input_embeddings(self):\n return self.deberta.get_input_embeddings()\n\n def set_input_embeddings(self, new_embeddings):\n self.deberta.set_input_embeddings(new_embeddings)\n\n\n @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format(\"batch_size, sequence_length\"))\n @add_code_sample_docstrings(\n tokenizer_class=_TOKENIZER_FOR_DOC,\n checkpoint=_CHECKPOINT_FOR_DOC,\n output_type=SequenceClassifierOutput,\n config_class=_CONFIG_FOR_DOC,\n )\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n inputs_embeds=None,\n labels=None,","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.set_input_embeddings","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.set_input_embeddings#L1266-L1267","kind":"function","name":"set_input_embeddings","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1266,"end_line":1267,"context_start_line":1246,"context_end_line":1287,"code":" def __init__(self, config):\n super().__init__(config)\n\n num_labels = getattr(config, \"num_labels\", 2)\n self.num_labels = num_labels\n\n self.deberta = DebertaV2Model(config)\n self.pooler = ContextPooler(config)\n output_dim = self.pooler.output_dim\n\n self.classifier = nn.Linear(output_dim, num_labels)\n drop_out = getattr(config, \"cls_dropout\", None)\n drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out\n self.dropout = StableDropout(drop_out)\n\n self.init_weights()\n\n def get_input_embeddings(self):\n return self.deberta.get_input_embeddings()\n\n def set_input_embeddings(self, new_embeddings):\n self.deberta.set_input_embeddings(new_embeddings)\n\n\n @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format(\"batch_size, sequence_length\"))\n @add_code_sample_docstrings(\n tokenizer_class=_TOKENIZER_FOR_DOC,\n checkpoint=_CHECKPOINT_FOR_DOC,\n output_type=SequenceClassifierOutput,\n config_class=_CONFIG_FOR_DOC,\n )\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n inputs_embeds=None,\n labels=None,\n output_attentions=None,\n output_hidden_states=None,\n return_dict=None,","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model._prune_heads","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model._prune_heads#L1008-L1013","kind":"function","name":"_prune_heads","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1008,"end_line":1013,"context_start_line":988,"context_end_line":1033,"code":" \"The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.\",\n DEBERTA_START_DOCSTRING,\n)\n# Copied from transformers.models.deberta.modeling_deberta.DebertaModel with Deberta->DebertaV2\nclass DebertaV2Model(DebertaV2PreTrainedModel):\n def __init__(self, config):\n super().__init__(config)\n\n self.embeddings = DebertaV2Embeddings(config)\n self.encoder = DebertaV2Encoder(config)\n self.z_steps = 0\n self.config = config\n self.init_weights()\n\n def get_input_embeddings(self):\n return self.embeddings.word_embeddings\n\n def set_input_embeddings(self, new_embeddings):\n self.embeddings.word_embeddings = new_embeddings\n\n def _prune_heads(self, heads_to_prune):\n \"\"\"\n Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base\n class PreTrainedModel\n \"\"\"\n raise NotImplementedError(\"The prune function is not implemented in DeBERTa model.\")\n\n\n @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format(\"batch_size, sequence_length\"))\n @add_code_sample_docstrings(\n tokenizer_class=_TOKENIZER_FOR_DOC,\n checkpoint=_CHECKPOINT_FOR_DOC,\n output_type=SequenceClassifierOutput,\n config_class=_CONFIG_FOR_DOC,\n )\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n inputs_embeds=None,\n output_attentions=None,\n output_hidden_states=None,\n return_dict=None,\n ):","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.get_output_embeddings","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.get_output_embeddings#L1119-L1120","kind":"function","name":"get_output_embeddings","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1119,"end_line":1120,"context_start_line":1099,"context_end_line":1140,"code":" attentions=encoder_outputs.attentions,\n )\n\n\n\n\n@add_start_docstrings(\"\"\"DeBERTa Model with a `language modeling` head on top. \"\"\", DEBERTA_START_DOCSTRING)\n# Copied from transformers.models.deberta.modeling_deberta.DebertaForMaskedLM with Deberta->DebertaV2\nclass DebertaV2ForMaskedLM(DebertaV2PreTrainedModel):\n _keys_to_ignore_on_load_unexpected = [r\"pooler\"]\n _keys_to_ignore_on_load_missing = [r\"position_ids\", r\"predictions.decoder.bias\"]\n\n def __init__(self, config):\n super().__init__(config)\n\n self.deberta = DebertaV2Model(config)\n self.cls = DebertaV2OnlyMLMHead(config)\n\n self.init_weights()\n\n def get_output_embeddings(self):\n return self.cls.predictions.decoder\n\n def set_output_embeddings(self, new_embeddings):\n self.cls.predictions.decoder = new_embeddings\n\n\n @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format(\"batch_size, sequence_length\"))\n @add_code_sample_docstrings(\n tokenizer_class=_TOKENIZER_FOR_DOC,\n checkpoint=_CHECKPOINT_FOR_DOC,\n output_type=MaskedLMOutput,\n config_class=_CONFIG_FOR_DOC,\n )\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n inputs_embeds=None,\n labels=None,","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.deberta_model.set_output_embeddings","uri":"program://CodeT/function/DIVERSE.code.src.deberta_model.set_output_embeddings#L1122-L1123","kind":"function","name":"set_output_embeddings","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1122,"end_line":1123,"context_start_line":1102,"context_end_line":1143,"code":"\n\n\n@add_start_docstrings(\"\"\"DeBERTa Model with a `language modeling` head on top. \"\"\", DEBERTA_START_DOCSTRING)\n# Copied from transformers.models.deberta.modeling_deberta.DebertaForMaskedLM with Deberta->DebertaV2\nclass DebertaV2ForMaskedLM(DebertaV2PreTrainedModel):\n _keys_to_ignore_on_load_unexpected = [r\"pooler\"]\n _keys_to_ignore_on_load_missing = [r\"position_ids\", r\"predictions.decoder.bias\"]\n\n def __init__(self, config):\n super().__init__(config)\n\n self.deberta = DebertaV2Model(config)\n self.cls = DebertaV2OnlyMLMHead(config)\n\n self.init_weights()\n\n def get_output_embeddings(self):\n return self.cls.predictions.decoder\n\n def set_output_embeddings(self, new_embeddings):\n self.cls.predictions.decoder = new_embeddings\n\n\n @add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format(\"batch_size, sequence_length\"))\n @add_code_sample_docstrings(\n tokenizer_class=_TOKENIZER_FOR_DOC,\n checkpoint=_CHECKPOINT_FOR_DOC,\n output_type=MaskedLMOutput,\n config_class=_CONFIG_FOR_DOC,\n )\n def forward(\n self,\n input_ids=None,\n attention_mask=None,\n token_type_ids=None,\n position_ids=None,\n inputs_embeds=None,\n labels=None,\n output_attentions=None,\n output_hidden_states=None,\n return_dict=None,","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.run_ner","uri":"program://CodeT/module/DIVERSE.code.src.run_ner#L1-L397","kind":"module","name":"DIVERSE.code.src.run_ner","path":"DIVERSE/code/src/run_ner.py","language":"python","start_line":1,"end_line":397,"context_start_line":1,"context_end_line":397,"code":"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" Fine-tuning the library models for named entity recognition on CoNLL-2003. \"\"\"\nimport sys\nimport logging\nimport os\nfrom dataclasses import dataclass, field\nfrom importlib import import_module\nfrom typing import Dict, List, Optional, Tuple\nimport pdb\nimport numpy as np\nfrom seqeval.metrics import accuracy_score, f1_score, precision_score, recall_score\nfrom torch import nn\nimport scipy\nfrom verifier_metrics import VerifierMetrics\nimport utils_io\nimport shutil\n\nimport transformers\nfrom transformers import (\n AutoConfig,\n AutoModelForTokenClassification,\n AutoTokenizer,\n DataCollatorWithPadding,\n EvalPrediction,\n HfArgumentParser,\n Trainer,\n TrainingArguments,\n set_seed,\n)\nfrom transformers.trainer_utils import is_main_process\nfrom utils_ner import Split, TokenClassificationDataset, TokenClassificationTask\nfrom deberta_model import DebertaV2ForTokenClassification\nimport pdb\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass ModelArguments:\n \"\"\"\n Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.\n \"\"\"\n model_name_or_path: str = field(\n metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n )\n dataset_name: str = field(\n metadata={\"help\": \"Name of the dataset to be run\"}\n )\n previous_run_dir: Optional[str] = field(\n default=None, metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n )\n previous_run_epoch: Optional[int] = field(\n default=1, metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n )\n config_name: Optional[str] = field(\n default=None, metadata={\"help\": \"Pretrained config name or path if not the same as model_name\"}\n )\n task_type: Optional[str] = field(\n default=\"NER\", metadata={\"help\": \"Task type to fine tune in training (e.g. NER, POS, etc)\"}\n )\n tokenizer_name: Optional[str] = field(\n default=None, metadata={\"help\": \"Pretrained tokenizer name or path if not the same as model_name\"}\n )\n use_fast: bool = field(default=False, metadata={\"help\": \"Set this flag to use fast tokenization.\"})\n # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,\n # or just modify its tokenizer_config.json.\n cache_dir: Optional[str] = field(\n default=None,\n metadata={\"help\": \"Where do you want to store the pretrained models downloaded from huggingface.co\"},\n )\n\n\n@dataclass\nclass DataTrainingArguments:\n \"\"\"\n Arguments pertaining to what data we are going to input our model for training and eval.\n \"\"\"\n train_data: str = field(\n metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n )\n test_data: str = field(\n metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n )\n data_labels: Optional[str] = field(\n default=\"labels.txt\",\n metadata={\"help\": \"Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.\"},\n )\n max_seq_length: int = field(\n default=512,\n metadata={\n \"help\": \"The maximum total input sequence length after tokenization. Sequences longer \"\n \"than this will be truncated, sequences shorter will be padded.\"\n },\n )\n overwrite_cache: bool = field(\n default=False, metadata={\"help\": \"Overwrite the cached training and evaluation sets\"}\n )\n alpha: Optional[float] = field(\n default=0.0, metadata={\"help\": \"help\"}\n )\n\n\n\ndef main():\n # See all possible arguments in src/transformers/training_args.py\n # or by passing the --help flag to this script.\n # We now keep distinct sets of args, for a cleaner separation of concerns.\n\n parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))\n if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n # If we pass only one argument to the script and it's the path to a json file,\n # let's parse it to get our arguments.\n model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n else:\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n if (\n os.path.exists(training_args.output_dir)\n and os.listdir(training_args.output_dir)\n and training_args.do_train\n and not training_args.overwrite_output_dir\n ):\n raise ValueError(\n f\"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome.\"\n )\n\n module = import_module(\"tasks\")\n try:\n token_classification_task_clazz = getattr(module, model_args.task_type)\n token_classification_task: TokenClassificationTask = token_classification_task_clazz()\n except AttributeError:\n raise ValueError(\n f\"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. \"\n f\"Available tasks classes are: {TokenClassificationTask.__subclasses__()}\"\n )\n\n # Setup logging\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,\n )\n logger.warning(\n \"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s\",\n training_args.local_rank,\n training_args.device,\n training_args.n_gpu,\n bool(training_args.local_rank != -1),\n training_args.fp16,\n )\n # Set the verbosity to info of the Transformers logger (on main process only):\n if is_main_process(training_args.local_rank):\n transformers.utils.logging.set_verbosity_info()\n transformers.utils.logging.enable_default_handler()\n transformers.utils.logging.enable_explicit_format()\n logger.info(\"Training/evaluation parameters %s\", training_args)\n\n # Set seed\n set_seed(training_args.seed)\n\n # Prepare CONLL-2003 task\n labels = token_classification_task.get_labels(data_args.data_labels)\n label_map: Dict[int, str] = {i: label for i, label in enumerate(labels)}\n num_labels = len(labels)\n\n # Load pretrained model and tokenizer\n #\n # Distributed training:\n # The .from_pretrained methods guarantee that only one local process can concurrently\n # download model & vocab.\n\n if model_args.previous_run_dir is not None:\n ckpt_path_list = [x for x in os.listdir(model_args.previous_run_dir) if \"checkpoint\" in x]\n ckpt_path_list = sorted(ckpt_path_list, key=lambda x : int(x.split(\"-\")[1]))\n load_model_dir = ckpt_path_list[model_args.previous_run_epoch - 1] # index starts from 0\n model_args.model_name_or_path = os.path.join(model_args.previous_run_dir, load_model_dir)\n\n config = AutoConfig.from_pretrained(\n model_args.config_name if model_args.config_name else model_args.model_name_or_path,\n num_labels=num_labels,\n id2label=label_map,\n label2id={label: i for i, label in enumerate(labels)},\n cache_dir=model_args.cache_dir,\n )\n # pdb.set_trace()\n\n # code change begin\n config.task_specific_params = {}\n config.task_specific_params[\"solution_correct_loss_weight\"] = 1.0\n config.task_specific_params[\"solution_incorrect_loss_weight\"] = 1.0\n config.task_specific_params[\"step_correct_loss_weight\"] = data_args.alpha\n config.task_specific_params[\"step_incorrect_loss_weight\"] = data_args.alpha\n config.task_specific_params[\"other_label_loss_weight\"] = 0.0\n # code change end\n\n print(\"alpha:\", data_args.alpha)\n print(\"alpha:\", config.task_specific_params[\"step_correct_loss_weight\"])\n\n tokenizer = AutoTokenizer.from_pretrained(\n model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,\n cache_dir=model_args.cache_dir,\n use_fast=model_args.use_fast,\n )\n model = DebertaV2ForTokenClassification.from_pretrained(\n model_args.model_name_or_path,\n from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n config=config,\n cache_dir=model_args.cache_dir,\n )\n \n # data_dir = data_args.train_data.replace(\"train.txt\", \"\") # for debug use\n data_dir = os.path.join(training_args.output_dir, \"data/\")\n print(\"[data_dir]:\", data_dir)\n os.makedirs(data_dir, exist_ok=True)\n\n shutil.copy(utils_io.get_file(data_args.train_data), data_dir)\n print(f\"train file copied to: {data_dir}\")\n shutil.copy(utils_io.get_file(data_args.test_data), data_dir + \"dev.txt\")\n print(f\"dev file copied to: {data_dir}\")\n shutil.copy(utils_io.get_file(data_args.test_data), data_dir)\n print(f\"test file copied to: {data_dir}\")\n shutil.copy(utils_io.get_file(data_args.data_labels), data_dir)\n print(f\"labels file copied to: {data_dir}\")\n\n # Get datasets\n train_dataset = (\n TokenClassificationDataset(\n token_classification_task=token_classification_task,\n data_dir=data_dir,\n tokenizer=tokenizer,\n labels=labels,\n model_type=config.model_type,\n max_seq_length=data_args.max_seq_length,\n overwrite_cache=data_args.overwrite_cache,\n mode=Split.train,\n )\n if training_args.do_train\n else None\n )\n eval_dataset = (\n TokenClassificationDataset(\n token_classification_task=token_classification_task,\n data_dir=data_dir,\n tokenizer=tokenizer,\n labels=labels,\n model_type=config.model_type,\n max_seq_length=data_args.max_seq_length,\n overwrite_cache=data_args.overwrite_cache,\n mode=Split.dev,\n )\n if training_args.do_eval\n else None\n )\n\n # save the texual sequences of eval dataset\n eval_sequences = [tokenizer.decode(x.input_ids) for x in eval_dataset]\n first_test_case_question = eval_sequences[0].split(\"&&\")[-1].strip()\n pred_num_per_case = 0\n for i, seq in enumerate(eval_sequences[1:]):\n if seq.split(\"&&\")[-1].strip() == first_test_case_question:\n pred_num_per_case += 1\n else:\n break\n print(\"pred_num_per_case:\", pred_num_per_case)\n \n\n def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]:\n preds = np.argmax(predictions, axis=2)\n\n batch_size, seq_len = preds.shape\n\n out_label_list = [[] for _ in range(batch_size)]\n preds_list = [[] for _ in range(batch_size)]\n\n for i in range(batch_size):\n for j in range(seq_len):\n if j == 1: # only pick the second index\n # if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:\n out_label_list[i].append(label_map[label_ids[i][j]])\n preds_list[i].append(label_map[preds[i][j]])\n return preds_list, out_label_list\n\n def get_solution_logits(predictions: np.ndarray):\n scores = []\n for i in range(predictions.shape[0]):\n solution_correct_index = config.label2id[\"SOLUTION-CORRECT\"]\n score = scipy.special.softmax(predictions[i][1])[solution_correct_index].item()\n\n scores.append(score)\n return scores\n\n # gsm8k_metric = datasets.load_metric(\"./gsm8k_verifier_metrics\")\n metric = VerifierMetrics(\n eval_sequences=eval_sequences,\n pred_num_per_case=pred_num_per_case,\n dataset_name=model_args.dataset_name,\n )\n\n def compute_metrics(p: EvalPrediction) -> Dict:\n scores = get_solution_logits(p.predictions)\n return metric.compute(predictions=scores, references=scores)\n\n # Data collator\n data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) if training_args.fp16 else None\n\n # Initialize our Trainer\n trainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=train_dataset,\n eval_dataset=eval_dataset,\n compute_metrics=compute_metrics,\n data_collator=data_collator,\n )\n\n # Training\n if training_args.do_train:\n trainer.train(\n model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None\n )\n trainer.save_model()\n # For convenience, we also re-save the tokenizer to the same directory,\n # so that you can share your model easily on huggingface.co/models =)\n if trainer.is_world_process_zero():\n tokenizer.save_pretrained(training_args.output_dir)\n\n # Evaluation\n results = {}\n if training_args.do_eval:\n logger.info(\"*** Evaluate ***\")\n\n result = trainer.evaluate()\n\n output_eval_file = os.path.join(training_args.output_dir, \"eval_results.txt\")\n if trainer.is_world_process_zero():\n with open(output_eval_file, \"w\") as writer:\n logger.info(\"***** Eval results *****\")\n for key, value in result.items():\n logger.info(\" %s = %s\", key, value)\n writer.write(\"%s = %s\\n\" % (key, value))\n\n results.update(result)\n\n # Predict\n if training_args.do_predict:\n test_dataset = TokenClassificationDataset(\n token_classification_task=token_classification_task,\n data_dir=data_dir,\n tokenizer=tokenizer,\n labels=labels,\n model_type=config.model_type,\n max_seq_length=data_args.max_seq_length,\n overwrite_cache=data_args.overwrite_cache,\n mode=Split.test,\n )\n\n predictions, label_ids, metrics = trainer.predict(test_dataset)\n preds_list, _ = align_predictions(predictions, label_ids)\n\n output_test_results_file = os.path.join(training_args.output_dir, \"test_results.txt\")\n if trainer.is_world_process_zero():\n with open(output_test_results_file, \"w\") as writer:\n for key, value in metrics.items():\n logger.info(\" %s = %s\", key, value)\n writer.write(\"%s = %s\\n\" % (key, value))\n\n # Save predictions\n output_test_predictions_file = os.path.join(training_args.output_dir, \"test_predictions.txt\")\n if trainer.is_world_process_zero():\n with open(output_test_predictions_file, \"w\") as writer:\n with open(os.path.join(data_dir, \"test.txt\"), \"r\") as f:\n token_classification_task.write_predictions_to_file(writer, f, preds_list)\n\n return results\n\n\ndef _mp_fn(index):\n # For xla_spawn (TPUs)\n main()\n\n\nif __name__ == \"__main__\":\n main()","source_hash":"4b7c42efb152bbd5ec90f25edf0119a789e074460cc95761b1d4f6c7b78c0e04","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.run_ner.ModelArguments","uri":"program://CodeT/class/DIVERSE.code.src.run_ner.ModelArguments#L53-L84","kind":"class","name":"ModelArguments","path":"DIVERSE/code/src/run_ner.py","language":"python","start_line":53,"end_line":84,"context_start_line":33,"context_end_line":104,"code":"from transformers import (\n AutoConfig,\n AutoModelForTokenClassification,\n AutoTokenizer,\n DataCollatorWithPadding,\n EvalPrediction,\n HfArgumentParser,\n Trainer,\n TrainingArguments,\n set_seed,\n)\nfrom transformers.trainer_utils import is_main_process\nfrom utils_ner import Split, TokenClassificationDataset, TokenClassificationTask\nfrom deberta_model import DebertaV2ForTokenClassification\nimport pdb\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass ModelArguments:\n \"\"\"\n Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.\n \"\"\"\n model_name_or_path: str = field(\n metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n )\n dataset_name: str = field(\n metadata={\"help\": \"Name of the dataset to be run\"}\n )\n previous_run_dir: Optional[str] = field(\n default=None, metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n )\n previous_run_epoch: Optional[int] = field(\n default=1, metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n )\n config_name: Optional[str] = field(\n default=None, metadata={\"help\": \"Pretrained config name or path if not the same as model_name\"}\n )\n task_type: Optional[str] = field(\n default=\"NER\", metadata={\"help\": \"Task type to fine tune in training (e.g. NER, POS, etc)\"}\n )\n tokenizer_name: Optional[str] = field(\n default=None, metadata={\"help\": \"Pretrained tokenizer name or path if not the same as model_name\"}\n )\n use_fast: bool = field(default=False, metadata={\"help\": \"Set this flag to use fast tokenization.\"})\n # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,\n # or just modify its tokenizer_config.json.\n cache_dir: Optional[str] = field(\n default=None,\n metadata={\"help\": \"Where do you want to store the pretrained models downloaded from huggingface.co\"},\n )\n\n\n@dataclass\nclass DataTrainingArguments:\n \"\"\"\n Arguments pertaining to what data we are going to input our model for training and eval.\n \"\"\"\n train_data: str = field(\n metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n )\n test_data: str = field(\n metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n )\n data_labels: Optional[str] = field(\n default=\"labels.txt\",\n metadata={\"help\": \"Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.\"},\n )\n max_seq_length: int = field(\n default=512,\n metadata={","source_hash":"4b7c42efb152bbd5ec90f25edf0119a789e074460cc95761b1d4f6c7b78c0e04","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.run_ner.DataTrainingArguments","uri":"program://CodeT/class/DIVERSE.code.src.run_ner.DataTrainingArguments#L88-L114","kind":"class","name":"DataTrainingArguments","path":"DIVERSE/code/src/run_ner.py","language":"python","start_line":88,"end_line":114,"context_start_line":68,"context_end_line":134,"code":" )\n config_name: Optional[str] = field(\n default=None, metadata={\"help\": \"Pretrained config name or path if not the same as model_name\"}\n )\n task_type: Optional[str] = field(\n default=\"NER\", metadata={\"help\": \"Task type to fine tune in training (e.g. NER, POS, etc)\"}\n )\n tokenizer_name: Optional[str] = field(\n default=None, metadata={\"help\": \"Pretrained tokenizer name or path if not the same as model_name\"}\n )\n use_fast: bool = field(default=False, metadata={\"help\": \"Set this flag to use fast tokenization.\"})\n # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,\n # or just modify its tokenizer_config.json.\n cache_dir: Optional[str] = field(\n default=None,\n metadata={\"help\": \"Where do you want to store the pretrained models downloaded from huggingface.co\"},\n )\n\n\n@dataclass\nclass DataTrainingArguments:\n \"\"\"\n Arguments pertaining to what data we are going to input our model for training and eval.\n \"\"\"\n train_data: str = field(\n metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n )\n test_data: str = field(\n metadata={\"help\": \"Path to pretrained model or model identifier from huggingface.co/models\"}\n )\n data_labels: Optional[str] = field(\n default=\"labels.txt\",\n metadata={\"help\": \"Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.\"},\n )\n max_seq_length: int = field(\n default=512,\n metadata={\n \"help\": \"The maximum total input sequence length after tokenization. Sequences longer \"\n \"than this will be truncated, sequences shorter will be padded.\"\n },\n )\n overwrite_cache: bool = field(\n default=False, metadata={\"help\": \"Overwrite the cached training and evaluation sets\"}\n )\n alpha: Optional[float] = field(\n default=0.0, metadata={\"help\": \"help\"}\n )\n\n\n\ndef main():\n # See all possible arguments in src/transformers/training_args.py\n # or by passing the --help flag to this script.\n # We now keep distinct sets of args, for a cleaner separation of concerns.\n\n parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))\n if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n # If we pass only one argument to the script and it's the path to a json file,\n # let's parse it to get our arguments.\n model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n else:\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n if (\n os.path.exists(training_args.output_dir)\n and os.listdir(training_args.output_dir)\n and training_args.do_train","source_hash":"4b7c42efb152bbd5ec90f25edf0119a789e074460cc95761b1d4f6c7b78c0e04","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.run_ner.main","uri":"program://CodeT/function/DIVERSE.code.src.run_ner.main#L118-L388","kind":"function","name":"main","path":"DIVERSE/code/src/run_ner.py","language":"python","start_line":118,"end_line":388,"context_start_line":98,"context_end_line":397,"code":" data_labels: Optional[str] = field(\n default=\"labels.txt\",\n metadata={\"help\": \"Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.\"},\n )\n max_seq_length: int = field(\n default=512,\n metadata={\n \"help\": \"The maximum total input sequence length after tokenization. Sequences longer \"\n \"than this will be truncated, sequences shorter will be padded.\"\n },\n )\n overwrite_cache: bool = field(\n default=False, metadata={\"help\": \"Overwrite the cached training and evaluation sets\"}\n )\n alpha: Optional[float] = field(\n default=0.0, metadata={\"help\": \"help\"}\n )\n\n\n\ndef main():\n # See all possible arguments in src/transformers/training_args.py\n # or by passing the --help flag to this script.\n # We now keep distinct sets of args, for a cleaner separation of concerns.\n\n parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))\n if len(sys.argv) == 2 and sys.argv[1].endswith(\".json\"):\n # If we pass only one argument to the script and it's the path to a json file,\n # let's parse it to get our arguments.\n model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))\n else:\n model_args, data_args, training_args = parser.parse_args_into_dataclasses()\n\n if (\n os.path.exists(training_args.output_dir)\n and os.listdir(training_args.output_dir)\n and training_args.do_train\n and not training_args.overwrite_output_dir\n ):\n raise ValueError(\n f\"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome.\"\n )\n\n module = import_module(\"tasks\")\n try:\n token_classification_task_clazz = getattr(module, model_args.task_type)\n token_classification_task: TokenClassificationTask = token_classification_task_clazz()\n except AttributeError:\n raise ValueError(\n f\"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. \"\n f\"Available tasks classes are: {TokenClassificationTask.__subclasses__()}\"\n )\n\n # Setup logging\n logging.basicConfig(\n format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,\n )\n logger.warning(\n \"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s\",\n training_args.local_rank,\n training_args.device,\n training_args.n_gpu,\n bool(training_args.local_rank != -1),\n training_args.fp16,\n )\n # Set the verbosity to info of the Transformers logger (on main process only):\n if is_main_process(training_args.local_rank):\n transformers.utils.logging.set_verbosity_info()\n transformers.utils.logging.enable_default_handler()\n transformers.utils.logging.enable_explicit_format()\n logger.info(\"Training/evaluation parameters %s\", training_args)\n\n # Set seed\n set_seed(training_args.seed)\n\n # Prepare CONLL-2003 task\n labels = token_classification_task.get_labels(data_args.data_labels)\n label_map: Dict[int, str] = {i: label for i, label in enumerate(labels)}\n num_labels = len(labels)\n\n # Load pretrained model and tokenizer\n #\n # Distributed training:\n # The .from_pretrained methods guarantee that only one local process can concurrently\n # download model & vocab.\n\n if model_args.previous_run_dir is not None:\n ckpt_path_list = [x for x in os.listdir(model_args.previous_run_dir) if \"checkpoint\" in x]\n ckpt_path_list = sorted(ckpt_path_list, key=lambda x : int(x.split(\"-\")[1]))\n load_model_dir = ckpt_path_list[model_args.previous_run_epoch - 1] # index starts from 0\n model_args.model_name_or_path = os.path.join(model_args.previous_run_dir, load_model_dir)\n\n config = AutoConfig.from_pretrained(\n model_args.config_name if model_args.config_name else model_args.model_name_or_path,\n num_labels=num_labels,\n id2label=label_map,\n label2id={label: i for i, label in enumerate(labels)},\n cache_dir=model_args.cache_dir,\n )\n # pdb.set_trace()\n\n # code change begin\n config.task_specific_params = {}\n config.task_specific_params[\"solution_correct_loss_weight\"] = 1.0\n config.task_specific_params[\"solution_incorrect_loss_weight\"] = 1.0\n config.task_specific_params[\"step_correct_loss_weight\"] = data_args.alpha\n config.task_specific_params[\"step_incorrect_loss_weight\"] = data_args.alpha\n config.task_specific_params[\"other_label_loss_weight\"] = 0.0\n # code change end\n\n print(\"alpha:\", data_args.alpha)\n print(\"alpha:\", config.task_specific_params[\"step_correct_loss_weight\"])\n\n tokenizer = AutoTokenizer.from_pretrained(\n model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,\n cache_dir=model_args.cache_dir,\n use_fast=model_args.use_fast,\n )\n model = DebertaV2ForTokenClassification.from_pretrained(\n model_args.model_name_or_path,\n from_tf=bool(\".ckpt\" in model_args.model_name_or_path),\n config=config,\n cache_dir=model_args.cache_dir,\n )\n \n # data_dir = data_args.train_data.replace(\"train.txt\", \"\") # for debug use\n data_dir = os.path.join(training_args.output_dir, \"data/\")\n print(\"[data_dir]:\", data_dir)\n os.makedirs(data_dir, exist_ok=True)\n\n shutil.copy(utils_io.get_file(data_args.train_data), data_dir)\n print(f\"train file copied to: {data_dir}\")\n shutil.copy(utils_io.get_file(data_args.test_data), data_dir + \"dev.txt\")\n print(f\"dev file copied to: {data_dir}\")\n shutil.copy(utils_io.get_file(data_args.test_data), data_dir)\n print(f\"test file copied to: {data_dir}\")\n shutil.copy(utils_io.get_file(data_args.data_labels), data_dir)\n print(f\"labels file copied to: {data_dir}\")\n\n # Get datasets\n train_dataset = (\n TokenClassificationDataset(\n token_classification_task=token_classification_task,\n data_dir=data_dir,\n tokenizer=tokenizer,\n labels=labels,\n model_type=config.model_type,\n max_seq_length=data_args.max_seq_length,\n overwrite_cache=data_args.overwrite_cache,\n mode=Split.train,\n )\n if training_args.do_train\n else None\n )\n eval_dataset = (\n TokenClassificationDataset(\n token_classification_task=token_classification_task,\n data_dir=data_dir,\n tokenizer=tokenizer,\n labels=labels,\n model_type=config.model_type,\n max_seq_length=data_args.max_seq_length,\n overwrite_cache=data_args.overwrite_cache,\n mode=Split.dev,\n )\n if training_args.do_eval\n else None\n )\n\n # save the texual sequences of eval dataset\n eval_sequences = [tokenizer.decode(x.input_ids) for x in eval_dataset]\n first_test_case_question = eval_sequences[0].split(\"&&\")[-1].strip()\n pred_num_per_case = 0\n for i, seq in enumerate(eval_sequences[1:]):\n if seq.split(\"&&\")[-1].strip() == first_test_case_question:\n pred_num_per_case += 1\n else:\n break\n print(\"pred_num_per_case:\", pred_num_per_case)\n \n\n def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]:\n preds = np.argmax(predictions, axis=2)\n\n batch_size, seq_len = preds.shape\n\n out_label_list = [[] for _ in range(batch_size)]\n preds_list = [[] for _ in range(batch_size)]\n\n for i in range(batch_size):\n for j in range(seq_len):\n if j == 1: # only pick the second index\n # if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:\n out_label_list[i].append(label_map[label_ids[i][j]])\n preds_list[i].append(label_map[preds[i][j]])\n return preds_list, out_label_list\n\n def get_solution_logits(predictions: np.ndarray):\n scores = []\n for i in range(predictions.shape[0]):\n solution_correct_index = config.label2id[\"SOLUTION-CORRECT\"]\n score = scipy.special.softmax(predictions[i][1])[solution_correct_index].item()\n\n scores.append(score)\n return scores\n\n # gsm8k_metric = datasets.load_metric(\"./gsm8k_verifier_metrics\")\n metric = VerifierMetrics(\n eval_sequences=eval_sequences,\n pred_num_per_case=pred_num_per_case,\n dataset_name=model_args.dataset_name,\n )\n\n def compute_metrics(p: EvalPrediction) -> Dict:\n scores = get_solution_logits(p.predictions)\n return metric.compute(predictions=scores, references=scores)\n\n # Data collator\n data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) if training_args.fp16 else None\n\n # Initialize our Trainer\n trainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=train_dataset,\n eval_dataset=eval_dataset,\n compute_metrics=compute_metrics,\n data_collator=data_collator,\n )\n\n # Training\n if training_args.do_train:\n trainer.train(\n model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None\n )\n trainer.save_model()\n # For convenience, we also re-save the tokenizer to the same directory,\n # so that you can share your model easily on huggingface.co/models =)\n if trainer.is_world_process_zero():\n tokenizer.save_pretrained(training_args.output_dir)\n\n # Evaluation\n results = {}\n if training_args.do_eval:\n logger.info(\"*** Evaluate ***\")\n\n result = trainer.evaluate()\n\n output_eval_file = os.path.join(training_args.output_dir, \"eval_results.txt\")\n if trainer.is_world_process_zero():\n with open(output_eval_file, \"w\") as writer:\n logger.info(\"***** Eval results *****\")\n for key, value in result.items():\n logger.info(\" %s = %s\", key, value)\n writer.write(\"%s = %s\\n\" % (key, value))\n\n results.update(result)\n\n # Predict\n if training_args.do_predict:\n test_dataset = TokenClassificationDataset(\n token_classification_task=token_classification_task,\n data_dir=data_dir,\n tokenizer=tokenizer,\n labels=labels,\n model_type=config.model_type,\n max_seq_length=data_args.max_seq_length,\n overwrite_cache=data_args.overwrite_cache,\n mode=Split.test,\n )\n\n predictions, label_ids, metrics = trainer.predict(test_dataset)\n preds_list, _ = align_predictions(predictions, label_ids)\n\n output_test_results_file = os.path.join(training_args.output_dir, \"test_results.txt\")\n if trainer.is_world_process_zero():\n with open(output_test_results_file, \"w\") as writer:\n for key, value in metrics.items():\n logger.info(\" %s = %s\", key, value)\n writer.write(\"%s = %s\\n\" % (key, value))\n\n # Save predictions\n output_test_predictions_file = os.path.join(training_args.output_dir, \"test_predictions.txt\")\n if trainer.is_world_process_zero():\n with open(output_test_predictions_file, \"w\") as writer:\n with open(os.path.join(data_dir, \"test.txt\"), \"r\") as f:\n token_classification_task.write_predictions_to_file(writer, f, preds_list)\n\n return results\n\n\ndef _mp_fn(index):\n # For xla_spawn (TPUs)\n main()\n\n\nif __name__ == \"__main__\":\n main()","source_hash":"4b7c42efb152bbd5ec90f25edf0119a789e074460cc95761b1d4f6c7b78c0e04","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.run_ner._mp_fn","uri":"program://CodeT/function/DIVERSE.code.src.run_ner._mp_fn#L391-L393","kind":"function","name":"_mp_fn","path":"DIVERSE/code/src/run_ner.py","language":"python","start_line":391,"end_line":393,"context_start_line":371,"context_end_line":397,"code":" predictions, label_ids, metrics = trainer.predict(test_dataset)\n preds_list, _ = align_predictions(predictions, label_ids)\n\n output_test_results_file = os.path.join(training_args.output_dir, \"test_results.txt\")\n if trainer.is_world_process_zero():\n with open(output_test_results_file, \"w\") as writer:\n for key, value in metrics.items():\n logger.info(\" %s = %s\", key, value)\n writer.write(\"%s = %s\\n\" % (key, value))\n\n # Save predictions\n output_test_predictions_file = os.path.join(training_args.output_dir, \"test_predictions.txt\")\n if trainer.is_world_process_zero():\n with open(output_test_predictions_file, \"w\") as writer:\n with open(os.path.join(data_dir, \"test.txt\"), \"r\") as f:\n token_classification_task.write_predictions_to_file(writer, f, preds_list)\n\n return results\n\n\ndef _mp_fn(index):\n # For xla_spawn (TPUs)\n main()\n\n\nif __name__ == \"__main__\":\n main()","source_hash":"4b7c42efb152bbd5ec90f25edf0119a789e074460cc95761b1d4f6c7b78c0e04","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.run_ner.align_predictions","uri":"program://CodeT/function/DIVERSE.code.src.run_ner.align_predictions#L281-L295","kind":"function","name":"align_predictions","path":"DIVERSE/code/src/run_ner.py","language":"python","start_line":281,"end_line":295,"context_start_line":261,"context_end_line":315,"code":" max_seq_length=data_args.max_seq_length,\n overwrite_cache=data_args.overwrite_cache,\n mode=Split.dev,\n )\n if training_args.do_eval\n else None\n )\n\n # save the texual sequences of eval dataset\n eval_sequences = [tokenizer.decode(x.input_ids) for x in eval_dataset]\n first_test_case_question = eval_sequences[0].split(\"&&\")[-1].strip()\n pred_num_per_case = 0\n for i, seq in enumerate(eval_sequences[1:]):\n if seq.split(\"&&\")[-1].strip() == first_test_case_question:\n pred_num_per_case += 1\n else:\n break\n print(\"pred_num_per_case:\", pred_num_per_case)\n \n\n def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]:\n preds = np.argmax(predictions, axis=2)\n\n batch_size, seq_len = preds.shape\n\n out_label_list = [[] for _ in range(batch_size)]\n preds_list = [[] for _ in range(batch_size)]\n\n for i in range(batch_size):\n for j in range(seq_len):\n if j == 1: # only pick the second index\n # if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:\n out_label_list[i].append(label_map[label_ids[i][j]])\n preds_list[i].append(label_map[preds[i][j]])\n return preds_list, out_label_list\n\n def get_solution_logits(predictions: np.ndarray):\n scores = []\n for i in range(predictions.shape[0]):\n solution_correct_index = config.label2id[\"SOLUTION-CORRECT\"]\n score = scipy.special.softmax(predictions[i][1])[solution_correct_index].item()\n\n scores.append(score)\n return scores\n\n # gsm8k_metric = datasets.load_metric(\"./gsm8k_verifier_metrics\")\n metric = VerifierMetrics(\n eval_sequences=eval_sequences,\n pred_num_per_case=pred_num_per_case,\n dataset_name=model_args.dataset_name,\n )\n\n def compute_metrics(p: EvalPrediction) -> Dict:\n scores = get_solution_logits(p.predictions)\n return metric.compute(predictions=scores, references=scores)","source_hash":"4b7c42efb152bbd5ec90f25edf0119a789e074460cc95761b1d4f6c7b78c0e04","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.run_ner.get_solution_logits","uri":"program://CodeT/function/DIVERSE.code.src.run_ner.get_solution_logits#L297-L304","kind":"function","name":"get_solution_logits","path":"DIVERSE/code/src/run_ner.py","language":"python","start_line":297,"end_line":304,"context_start_line":277,"context_end_line":324,"code":" break\n print(\"pred_num_per_case:\", pred_num_per_case)\n \n\n def align_predictions(predictions: np.ndarray, label_ids: np.ndarray) -> Tuple[List[int], List[int]]:\n preds = np.argmax(predictions, axis=2)\n\n batch_size, seq_len = preds.shape\n\n out_label_list = [[] for _ in range(batch_size)]\n preds_list = [[] for _ in range(batch_size)]\n\n for i in range(batch_size):\n for j in range(seq_len):\n if j == 1: # only pick the second index\n # if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:\n out_label_list[i].append(label_map[label_ids[i][j]])\n preds_list[i].append(label_map[preds[i][j]])\n return preds_list, out_label_list\n\n def get_solution_logits(predictions: np.ndarray):\n scores = []\n for i in range(predictions.shape[0]):\n solution_correct_index = config.label2id[\"SOLUTION-CORRECT\"]\n score = scipy.special.softmax(predictions[i][1])[solution_correct_index].item()\n\n scores.append(score)\n return scores\n\n # gsm8k_metric = datasets.load_metric(\"./gsm8k_verifier_metrics\")\n metric = VerifierMetrics(\n eval_sequences=eval_sequences,\n pred_num_per_case=pred_num_per_case,\n dataset_name=model_args.dataset_name,\n )\n\n def compute_metrics(p: EvalPrediction) -> Dict:\n scores = get_solution_logits(p.predictions)\n return metric.compute(predictions=scores, references=scores)\n\n # Data collator\n data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) if training_args.fp16 else None\n\n # Initialize our Trainer\n trainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=train_dataset,","source_hash":"4b7c42efb152bbd5ec90f25edf0119a789e074460cc95761b1d4f6c7b78c0e04","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.run_ner.compute_metrics","uri":"program://CodeT/function/DIVERSE.code.src.run_ner.compute_metrics#L313-L315","kind":"function","name":"compute_metrics","path":"DIVERSE/code/src/run_ner.py","language":"python","start_line":313,"end_line":315,"context_start_line":293,"context_end_line":335,"code":" out_label_list[i].append(label_map[label_ids[i][j]])\n preds_list[i].append(label_map[preds[i][j]])\n return preds_list, out_label_list\n\n def get_solution_logits(predictions: np.ndarray):\n scores = []\n for i in range(predictions.shape[0]):\n solution_correct_index = config.label2id[\"SOLUTION-CORRECT\"]\n score = scipy.special.softmax(predictions[i][1])[solution_correct_index].item()\n\n scores.append(score)\n return scores\n\n # gsm8k_metric = datasets.load_metric(\"./gsm8k_verifier_metrics\")\n metric = VerifierMetrics(\n eval_sequences=eval_sequences,\n pred_num_per_case=pred_num_per_case,\n dataset_name=model_args.dataset_name,\n )\n\n def compute_metrics(p: EvalPrediction) -> Dict:\n scores = get_solution_logits(p.predictions)\n return metric.compute(predictions=scores, references=scores)\n\n # Data collator\n data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8) if training_args.fp16 else None\n\n # Initialize our Trainer\n trainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=train_dataset,\n eval_dataset=eval_dataset,\n compute_metrics=compute_metrics,\n data_collator=data_collator,\n )\n\n # Training\n if training_args.do_train:\n trainer.train(\n model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None\n )\n trainer.save_model()","source_hash":"4b7c42efb152bbd5ec90f25edf0119a789e074460cc95761b1d4f6c7b78c0e04","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.verifier_metrics","uri":"program://CodeT/module/DIVERSE.code.src.verifier_metrics#L1-L110","kind":"module","name":"DIVERSE.code.src.verifier_metrics","path":"DIVERSE/code/src/verifier_metrics.py","language":"python","start_line":1,"end_line":110,"context_start_line":1,"context_end_line":110,"code":"import absl # Here to have a nice missing dependency error message early on\nimport nltk # Here to have a nice missing dependency error message early on\nimport numpy # Here to have a nice missing dependency error message early on\nimport six # Here to have a nice missing dependency error message early on\nfrom rouge_score import rouge_scorer, scoring\nimport datasets\nimport pdb\nimport numpy as np\nimport scipy\nfrom tqdm import tqdm\n\nfrom utils import (\n GSM8KCase,\n GSM8KExample,\n TextEntailmentCase,\n TextEntailmentExample,\n convert_eval_sequences_to_cases,\n compute_results,\n compute_results_avg,\n)\n\n\ncase_class_map = {\n \"GSM8K\": GSM8KCase,\n \"CLUTRR\": TextEntailmentCase,\n \"strategyQA\": TextEntailmentCase,\n}\n\nexample_class_map = {\n \"GSM8K\": GSM8KExample,\n \"CLUTRR\": TextEntailmentExample,\n \"strategyQA\": TextEntailmentExample,\n}\n\n_CITATION = \"\"\n_DESCRIPTION = \"\"\n_KWARGS_DESCRIPTION = \"\"\n\n\ndef simple_accuracy(preds, labels):\n correct_case_num = 0\n for pred, label in zip(preds, labels):\n pred = pred.replace(\" \", \"\")\n label = label.replace(\" \", \"\")\n if pred == label:\n correct_case_num += 1\n return correct_case_num / len(preds)\n\n\n@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)\nclass VerifierMetrics(datasets.Metric):\n def __init__(self, eval_sequences=None, pred_num_per_case=None, dataset_name=None, **kwargs,):\n super().__init__(**kwargs)\n self.pred_num_per_case = pred_num_per_case\n self.cases = convert_eval_sequences_to_cases(\n eval_sequences=eval_sequences,\n pred_num_per_case=pred_num_per_case,\n case_class=case_class_map[dataset_name],\n example_class=example_class_map[dataset_name],\n )\n \n def assign_scores(self, predictions):\n for i in range(0, len(predictions), self.pred_num_per_case + 1):\n curr_case_index = i // (self.pred_num_per_case + 1)\n self.cases[curr_case_index].ground_truth.verifier_score = predictions[i]\n for j in range(0, self.pred_num_per_case):\n self.cases[curr_case_index].preds[j].verifier_score = predictions[i+j+1]\n\n def _compute(self, predictions=None, references=None):\n self.assign_scores(predictions)\n result = {}\n result.update(compute_results_avg(self.cases, rand_k=100, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=75, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=50, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=25, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=20, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=10, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=5, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=2, repeat_time=10))\n return result\n\n def _info(self):\n return datasets.MetricInfo(\n description=_DESCRIPTION,\n citation=_CITATION,\n inputs_description=_KWARGS_DESCRIPTION,\n features=datasets.Features(\n {\n \"predictions\": datasets.Value(\"float32\", id=\"scores\"),\n \"references\": datasets.Value(\"float32\", id=\"scores\"),\n }\n ),\n codebase_urls=[],\n reference_urls=[],\n )\n \n def _metric_info(self):\n return datasets.MetricInfo(\n description=_DESCRIPTION,\n citation=_CITATION,\n inputs_description=_KWARGS_DESCRIPTION,\n features=datasets.Features(\n {\n \"predictions\": datasets.Value(\"string\", id=\"sequence\"),\n \"references\": datasets.Value(\"string\", id=\"sequence\"),\n }\n ),\n codebase_urls=[],\n reference_urls=[],\n )","source_hash":"e167936509daa986498fce3a20defb189642acfa5c6e1469e18c6b009399a459","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.verifier_metrics.simple_accuracy","uri":"program://CodeT/function/DIVERSE.code.src.verifier_metrics.simple_accuracy#L40-L47","kind":"function","name":"simple_accuracy","path":"DIVERSE/code/src/verifier_metrics.py","language":"python","start_line":40,"end_line":47,"context_start_line":20,"context_end_line":67,"code":")\n\n\ncase_class_map = {\n \"GSM8K\": GSM8KCase,\n \"CLUTRR\": TextEntailmentCase,\n \"strategyQA\": TextEntailmentCase,\n}\n\nexample_class_map = {\n \"GSM8K\": GSM8KExample,\n \"CLUTRR\": TextEntailmentExample,\n \"strategyQA\": TextEntailmentExample,\n}\n\n_CITATION = \"\"\n_DESCRIPTION = \"\"\n_KWARGS_DESCRIPTION = \"\"\n\n\ndef simple_accuracy(preds, labels):\n correct_case_num = 0\n for pred, label in zip(preds, labels):\n pred = pred.replace(\" \", \"\")\n label = label.replace(\" \", \"\")\n if pred == label:\n correct_case_num += 1\n return correct_case_num / len(preds)\n\n\n@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)\nclass VerifierMetrics(datasets.Metric):\n def __init__(self, eval_sequences=None, pred_num_per_case=None, dataset_name=None, **kwargs,):\n super().__init__(**kwargs)\n self.pred_num_per_case = pred_num_per_case\n self.cases = convert_eval_sequences_to_cases(\n eval_sequences=eval_sequences,\n pred_num_per_case=pred_num_per_case,\n case_class=case_class_map[dataset_name],\n example_class=example_class_map[dataset_name],\n )\n \n def assign_scores(self, predictions):\n for i in range(0, len(predictions), self.pred_num_per_case + 1):\n curr_case_index = i // (self.pred_num_per_case + 1)\n self.cases[curr_case_index].ground_truth.verifier_score = predictions[i]\n for j in range(0, self.pred_num_per_case):\n self.cases[curr_case_index].preds[j].verifier_score = predictions[i+j+1]","source_hash":"e167936509daa986498fce3a20defb189642acfa5c6e1469e18c6b009399a459","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.verifier_metrics.VerifierMetrics","uri":"program://CodeT/class/DIVERSE.code.src.verifier_metrics.VerifierMetrics#L51-L110","kind":"class","name":"VerifierMetrics","path":"DIVERSE/code/src/verifier_metrics.py","language":"python","start_line":51,"end_line":110,"context_start_line":31,"context_end_line":110,"code":" \"CLUTRR\": TextEntailmentExample,\n \"strategyQA\": TextEntailmentExample,\n}\n\n_CITATION = \"\"\n_DESCRIPTION = \"\"\n_KWARGS_DESCRIPTION = \"\"\n\n\ndef simple_accuracy(preds, labels):\n correct_case_num = 0\n for pred, label in zip(preds, labels):\n pred = pred.replace(\" \", \"\")\n label = label.replace(\" \", \"\")\n if pred == label:\n correct_case_num += 1\n return correct_case_num / len(preds)\n\n\n@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)\nclass VerifierMetrics(datasets.Metric):\n def __init__(self, eval_sequences=None, pred_num_per_case=None, dataset_name=None, **kwargs,):\n super().__init__(**kwargs)\n self.pred_num_per_case = pred_num_per_case\n self.cases = convert_eval_sequences_to_cases(\n eval_sequences=eval_sequences,\n pred_num_per_case=pred_num_per_case,\n case_class=case_class_map[dataset_name],\n example_class=example_class_map[dataset_name],\n )\n \n def assign_scores(self, predictions):\n for i in range(0, len(predictions), self.pred_num_per_case + 1):\n curr_case_index = i // (self.pred_num_per_case + 1)\n self.cases[curr_case_index].ground_truth.verifier_score = predictions[i]\n for j in range(0, self.pred_num_per_case):\n self.cases[curr_case_index].preds[j].verifier_score = predictions[i+j+1]\n\n def _compute(self, predictions=None, references=None):\n self.assign_scores(predictions)\n result = {}\n result.update(compute_results_avg(self.cases, rand_k=100, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=75, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=50, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=25, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=20, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=10, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=5, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=2, repeat_time=10))\n return result\n\n def _info(self):\n return datasets.MetricInfo(\n description=_DESCRIPTION,\n citation=_CITATION,\n inputs_description=_KWARGS_DESCRIPTION,\n features=datasets.Features(\n {\n \"predictions\": datasets.Value(\"float32\", id=\"scores\"),\n \"references\": datasets.Value(\"float32\", id=\"scores\"),\n }\n ),\n codebase_urls=[],\n reference_urls=[],\n )\n \n def _metric_info(self):\n return datasets.MetricInfo(\n description=_DESCRIPTION,\n citation=_CITATION,\n inputs_description=_KWARGS_DESCRIPTION,\n features=datasets.Features(\n {\n \"predictions\": datasets.Value(\"string\", id=\"sequence\"),\n \"references\": datasets.Value(\"string\", id=\"sequence\"),\n }\n ),\n codebase_urls=[],\n reference_urls=[],\n )","source_hash":"e167936509daa986498fce3a20defb189642acfa5c6e1469e18c6b009399a459","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.verifier_metrics.__init__","uri":"program://CodeT/function/DIVERSE.code.src.verifier_metrics.__init__#L52-L60","kind":"function","name":"__init__","path":"DIVERSE/code/src/verifier_metrics.py","language":"python","start_line":52,"end_line":60,"context_start_line":32,"context_end_line":80,"code":" \"strategyQA\": TextEntailmentExample,\n}\n\n_CITATION = \"\"\n_DESCRIPTION = \"\"\n_KWARGS_DESCRIPTION = \"\"\n\n\ndef simple_accuracy(preds, labels):\n correct_case_num = 0\n for pred, label in zip(preds, labels):\n pred = pred.replace(\" \", \"\")\n label = label.replace(\" \", \"\")\n if pred == label:\n correct_case_num += 1\n return correct_case_num / len(preds)\n\n\n@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)\nclass VerifierMetrics(datasets.Metric):\n def __init__(self, eval_sequences=None, pred_num_per_case=None, dataset_name=None, **kwargs,):\n super().__init__(**kwargs)\n self.pred_num_per_case = pred_num_per_case\n self.cases = convert_eval_sequences_to_cases(\n eval_sequences=eval_sequences,\n pred_num_per_case=pred_num_per_case,\n case_class=case_class_map[dataset_name],\n example_class=example_class_map[dataset_name],\n )\n \n def assign_scores(self, predictions):\n for i in range(0, len(predictions), self.pred_num_per_case + 1):\n curr_case_index = i // (self.pred_num_per_case + 1)\n self.cases[curr_case_index].ground_truth.verifier_score = predictions[i]\n for j in range(0, self.pred_num_per_case):\n self.cases[curr_case_index].preds[j].verifier_score = predictions[i+j+1]\n\n def _compute(self, predictions=None, references=None):\n self.assign_scores(predictions)\n result = {}\n result.update(compute_results_avg(self.cases, rand_k=100, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=75, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=50, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=25, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=20, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=10, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=5, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=2, repeat_time=10))\n return result","source_hash":"e167936509daa986498fce3a20defb189642acfa5c6e1469e18c6b009399a459","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.verifier_metrics.assign_scores","uri":"program://CodeT/function/DIVERSE.code.src.verifier_metrics.assign_scores#L62-L67","kind":"function","name":"assign_scores","path":"DIVERSE/code/src/verifier_metrics.py","language":"python","start_line":62,"end_line":67,"context_start_line":42,"context_end_line":87,"code":" for pred, label in zip(preds, labels):\n pred = pred.replace(\" \", \"\")\n label = label.replace(\" \", \"\")\n if pred == label:\n correct_case_num += 1\n return correct_case_num / len(preds)\n\n\n@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)\nclass VerifierMetrics(datasets.Metric):\n def __init__(self, eval_sequences=None, pred_num_per_case=None, dataset_name=None, **kwargs,):\n super().__init__(**kwargs)\n self.pred_num_per_case = pred_num_per_case\n self.cases = convert_eval_sequences_to_cases(\n eval_sequences=eval_sequences,\n pred_num_per_case=pred_num_per_case,\n case_class=case_class_map[dataset_name],\n example_class=example_class_map[dataset_name],\n )\n \n def assign_scores(self, predictions):\n for i in range(0, len(predictions), self.pred_num_per_case + 1):\n curr_case_index = i // (self.pred_num_per_case + 1)\n self.cases[curr_case_index].ground_truth.verifier_score = predictions[i]\n for j in range(0, self.pred_num_per_case):\n self.cases[curr_case_index].preds[j].verifier_score = predictions[i+j+1]\n\n def _compute(self, predictions=None, references=None):\n self.assign_scores(predictions)\n result = {}\n result.update(compute_results_avg(self.cases, rand_k=100, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=75, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=50, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=25, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=20, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=10, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=5, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=2, repeat_time=10))\n return result\n\n def _info(self):\n return datasets.MetricInfo(\n description=_DESCRIPTION,\n citation=_CITATION,\n inputs_description=_KWARGS_DESCRIPTION,\n features=datasets.Features(","source_hash":"e167936509daa986498fce3a20defb189642acfa5c6e1469e18c6b009399a459","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.verifier_metrics._compute","uri":"program://CodeT/function/DIVERSE.code.src.verifier_metrics._compute#L69-L80","kind":"function","name":"_compute","path":"DIVERSE/code/src/verifier_metrics.py","language":"python","start_line":69,"end_line":80,"context_start_line":49,"context_end_line":100,"code":"\n@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)\nclass VerifierMetrics(datasets.Metric):\n def __init__(self, eval_sequences=None, pred_num_per_case=None, dataset_name=None, **kwargs,):\n super().__init__(**kwargs)\n self.pred_num_per_case = pred_num_per_case\n self.cases = convert_eval_sequences_to_cases(\n eval_sequences=eval_sequences,\n pred_num_per_case=pred_num_per_case,\n case_class=case_class_map[dataset_name],\n example_class=example_class_map[dataset_name],\n )\n \n def assign_scores(self, predictions):\n for i in range(0, len(predictions), self.pred_num_per_case + 1):\n curr_case_index = i // (self.pred_num_per_case + 1)\n self.cases[curr_case_index].ground_truth.verifier_score = predictions[i]\n for j in range(0, self.pred_num_per_case):\n self.cases[curr_case_index].preds[j].verifier_score = predictions[i+j+1]\n\n def _compute(self, predictions=None, references=None):\n self.assign_scores(predictions)\n result = {}\n result.update(compute_results_avg(self.cases, rand_k=100, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=75, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=50, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=25, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=20, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=10, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=5, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=2, repeat_time=10))\n return result\n\n def _info(self):\n return datasets.MetricInfo(\n description=_DESCRIPTION,\n citation=_CITATION,\n inputs_description=_KWARGS_DESCRIPTION,\n features=datasets.Features(\n {\n \"predictions\": datasets.Value(\"float32\", id=\"scores\"),\n \"references\": datasets.Value(\"float32\", id=\"scores\"),\n }\n ),\n codebase_urls=[],\n reference_urls=[],\n )\n \n def _metric_info(self):\n return datasets.MetricInfo(\n description=_DESCRIPTION,\n citation=_CITATION,","source_hash":"e167936509daa986498fce3a20defb189642acfa5c6e1469e18c6b009399a459","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.verifier_metrics._info","uri":"program://CodeT/function/DIVERSE.code.src.verifier_metrics._info#L82-L95","kind":"function","name":"_info","path":"DIVERSE/code/src/verifier_metrics.py","language":"python","start_line":82,"end_line":95,"context_start_line":62,"context_end_line":110,"code":" def assign_scores(self, predictions):\n for i in range(0, len(predictions), self.pred_num_per_case + 1):\n curr_case_index = i // (self.pred_num_per_case + 1)\n self.cases[curr_case_index].ground_truth.verifier_score = predictions[i]\n for j in range(0, self.pred_num_per_case):\n self.cases[curr_case_index].preds[j].verifier_score = predictions[i+j+1]\n\n def _compute(self, predictions=None, references=None):\n self.assign_scores(predictions)\n result = {}\n result.update(compute_results_avg(self.cases, rand_k=100, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=75, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=50, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=25, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=20, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=10, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=5, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=2, repeat_time=10))\n return result\n\n def _info(self):\n return datasets.MetricInfo(\n description=_DESCRIPTION,\n citation=_CITATION,\n inputs_description=_KWARGS_DESCRIPTION,\n features=datasets.Features(\n {\n \"predictions\": datasets.Value(\"float32\", id=\"scores\"),\n \"references\": datasets.Value(\"float32\", id=\"scores\"),\n }\n ),\n codebase_urls=[],\n reference_urls=[],\n )\n \n def _metric_info(self):\n return datasets.MetricInfo(\n description=_DESCRIPTION,\n citation=_CITATION,\n inputs_description=_KWARGS_DESCRIPTION,\n features=datasets.Features(\n {\n \"predictions\": datasets.Value(\"string\", id=\"sequence\"),\n \"references\": datasets.Value(\"string\", id=\"sequence\"),\n }\n ),\n codebase_urls=[],\n reference_urls=[],\n )","source_hash":"e167936509daa986498fce3a20defb189642acfa5c6e1469e18c6b009399a459","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.verifier_metrics._metric_info","uri":"program://CodeT/function/DIVERSE.code.src.verifier_metrics._metric_info#L97-L110","kind":"function","name":"_metric_info","path":"DIVERSE/code/src/verifier_metrics.py","language":"python","start_line":97,"end_line":110,"context_start_line":77,"context_end_line":110,"code":" result.update(compute_results_avg(self.cases, rand_k=10, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=5, repeat_time=10))\n result.update(compute_results_avg(self.cases, rand_k=2, repeat_time=10))\n return result\n\n def _info(self):\n return datasets.MetricInfo(\n description=_DESCRIPTION,\n citation=_CITATION,\n inputs_description=_KWARGS_DESCRIPTION,\n features=datasets.Features(\n {\n \"predictions\": datasets.Value(\"float32\", id=\"scores\"),\n \"references\": datasets.Value(\"float32\", id=\"scores\"),\n }\n ),\n codebase_urls=[],\n reference_urls=[],\n )\n \n def _metric_info(self):\n return datasets.MetricInfo(\n description=_DESCRIPTION,\n citation=_CITATION,\n inputs_description=_KWARGS_DESCRIPTION,\n features=datasets.Features(\n {\n \"predictions\": datasets.Value(\"string\", id=\"sequence\"),\n \"references\": datasets.Value(\"string\", id=\"sequence\"),\n }\n ),\n codebase_urls=[],\n reference_urls=[],\n )","source_hash":"e167936509daa986498fce3a20defb189642acfa5c6e1469e18c6b009399a459","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.verifier_data_prepare","uri":"program://CodeT/module/DIVERSE.code.src.verifier_data_prepare#L1-L227","kind":"module","name":"DIVERSE.code.src.verifier_data_prepare","path":"DIVERSE/code/src/verifier_data_prepare.py","language":"python","start_line":1,"end_line":227,"context_start_line":1,"context_end_line":227,"code":"import os\nimport json\nimport random\nimport argparse\nfrom tqdm import tqdm\nimport re\nimport utils_io\nfrom utils import (\n GSM8KCase,\n TextEntailmentCase,\n GSM8KExample,\n TextEntailmentExample,\n compute_top1_and_recall,\n post_process_answer_clutrr_mapping,\n post_process_answer_clutrr_cutoff,\n)\nfrom transformers import (\n AutoTokenizer,\n AutoModelForSequenceClassification,\n)\nimport torch\nimport pdb\nimport logging\n\n\nlogger = logging.getLogger(__name__)\n\ncase_class_map = {\n \"GSM8K\": GSM8KCase,\n \"CLUTRR\": TextEntailmentCase,\n \"strategyQA\": TextEntailmentCase,\n}\n\nexample_class_map = {\n \"GSM8K\": GSM8KExample,\n \"CLUTRR\": TextEntailmentExample,\n \"strategyQA\": TextEntailmentExample,\n}\n\nrelation_reverse_map = {\n 'sister': ['brother'],\n 'son': ['father', 'mother'],\n 'aunt': ['nephew', 'niece'],\n 'granddaughter': ['grandfather', 'grandmother'],\n 'father': ['son', 'daughter'],\n 'grandfather': ['grandson', 'granddaughter'],\n 'grandmother': ['grandson', 'granddaughter'],\n 'mother-in-law': ['son-in-law', 'daughter-in-law'],\n 'uncle': ['nephew', 'niece'],\n 'niece': ['uncle', 'aunt'],\n 'mother': ['son', 'daughter'],\n 'brother': ['sister'],\n 'daughter': ['father', 'mother'],\n 'nephew': ['uncle', 'aunt'],\n 'grandson': ['grandfather', 'grandmother'],\n 'son-in-law': ['father-in-law', 'mother-in-law'],\n 'father-in-law': ['son-in-law', 'daughter-in-law'],\n 'daughter-in-law': ['father-in-law', 'mother-in-law'],\n}\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n# device = \"cpu\"\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--generator_result_file\", type=str, default=None, help=\"generator output file in .jsonl format\")\n parser.add_argument(\"--output_dir\", type=str, default=None, help=\"output dir\")\n parser.add_argument(\"--random_seed\", type=int, default=233, help=\"random_seed\")\n parser.add_argument(\"--split\", type=str, default=\"train\", help=\"split (train or test)\")\n parser.add_argument(\"--dataset_name\", type=str, default=\"GSM8K\", help=\"GSM8K, CLUTRR, strategyQA\")\n parser.add_argument(\"--text_entailment_model_name\", type=str, default=\"roberta-large-mnli\", help=\"roberta-large-mnli, facebook/bart-large-mnli, etc.\")\n parser.add_argument(\"--text_entailment_batch_size\", type=int, default=512, help=\"text entailment batch size\")\n args = parser.parse_args()\n\n random.seed(args.random_seed)\n\n if args.dataset_name != \"GSM8K\":\n logger.info(\"Loading textual entailment models...\")\n model = AutoModelForSequenceClassification.from_pretrained(args.text_entailment_model_name).to(device)\n model.eval()\n tokenizer = AutoTokenizer.from_pretrained(args.text_entailment_model_name)\n else:\n model = None\n tokenizer = None\n \n # loading data from generator output result file\n generator_outputs = [json.loads(line) for line in open(utils_io.get_file(args.generator_result_file))]\n question_to_ground_truth = {}\n\n # prompt data make up\n prompt_data = []\n for generator_output in generator_outputs:\n context = generator_output[\"context\"]\n samples = generator_output[\"samples\"]\n for sample in samples:\n metadata = generator_output[\"metadata\"]\n prompt_data.append({\"context\": context, \"sample\": sample, \"metadata\": metadata})\n\n prompt_data_dict = {}\n\n # some pre-processing about formulas and answers for GSM8K and other datasets\n for obj in tqdm(prompt_data):\n question = obj[\"metadata\"][\"question\"].strip().replace(\"\\n\", \"\")\n def extract_solution(sample):\n sample = sample.strip()\n if '####' in sample:\n stop = sample.find('\\n\\n', sample.index('####'))\n if stop >= 0:\n sample = sample[:stop]\n sample = sample.replace('\\n\\n', '\\n')\n return sample\n sample = extract_solution(obj[\"sample\"])\n sample = sample.strip().replace(\"\\n\", \"%%\") # for sequence labeling\n ground_truth = obj[\"metadata\"][\"ground_truth\"].strip().replace(\"\\n\\n\", \"\\n\").replace(\"\\n\", \"%%\") # for sequence labeling\n if args.dataset_name == \"GSM8K\":\n if \"####\" not in sample:\n reg = \"<<.+>>[\\d\\.]+\"\n eqs = re.findall(reg, sample)\n if len(eqs) > 0:\n final_answer = eqs[-1].split(\">>\")[-1].strip()\n if final_answer and len(final_answer) > 0 and final_answer[-1] == '.':\n final_answer = final_answer[:-1]\n if sample[-2:] == \"%%\":\n sample = sample + \"####\" + final_answer\n else:\n sample = sample + \"%%####\" + final_answer\n elif args.dataset_name == \"CLUTRR\":\n pass\n if \"####\" not in sample:\n reg = \"the.+?of\"\n eqs = re.findall(reg, sample)\n if len(eqs) > 0:\n final_answer = eqs[-1].replace(\"the \", \"\").replace(\" of\", \"\")\n if sample[-2:] == \"%%\":\n sample = sample + \"####\" + final_answer\n else:\n sample = sample + \"%%####\" + final_answer\n if question not in prompt_data_dict:\n prompt_data_dict[question] = []\n \n sample = sample.replace(\"\\n\", \"%%\") # for sequence labeling\n ground_truth = ground_truth.replace(\"\\n\", \"%%\") # for sequence labeling\n question_to_ground_truth[question] = ground_truth\n prompt_data_dict[question].append(sample)\n\n # # code change\n # if args.dataset_name == \"CLUTRR\":\n # if \"####\" not in sample:\n # continue\n # sample_body, sample_answer = sample.split(\"####\")[0].strip(), sample.split(\"####\")[-1].strip()\n # # pdb.set_trace()\n # if sample_answer in relation_reverse_map:\n # for reverse in relation_reverse_map[sample_answer]:\n # prompt_data_dict[question].append(sample_body + \"####\" + reverse)\n \n # check the least sample num among all the cases\n min_sample_num_per_case = 99999999\n for k in prompt_data_dict:\n min_sample_num_per_case = min(min_sample_num_per_case, len(prompt_data_dict[k]))\n\n # converting data into Case\n prompt_cases = []\n for k in prompt_data_dict:\n case = case_class_map[args.dataset_name](\"\", [])\n case.question = k\n case.ground_truth = example_class_map[args.dataset_name](question_to_ground_truth[k])\n case.entailment_batch_size = args.text_entailment_batch_size\n for sample_idx, x in enumerate(prompt_data_dict[k]):\n if sample_idx >= min_sample_num_per_case:\n break\n pred = example_class_map[args.dataset_name](x)\n case.preds.append(pred)\n prompt_cases.append(case)\n print(f\"Total cases: {len(prompt_cases)}\".replace(\"\\n\", \"\\\\n\"))\n print(f\"Case 0's question: {prompt_cases[0].question}\".replace(\"\\n\", \"\\\\n\"))\n print(f\"Case 0's ground truth: {prompt_cases[0].ground_truth.content}\".replace(\"\\n\", \"\\\\n\"))\n print(f\"Case 0's sample0: {prompt_cases[0].preds[0].content}\".replace(\"\\n\", \"\\\\n\"))\n\n # print the random top1 and recall of the data\n print(\"*********** Data statistics ***********\")\n res = compute_top1_and_recall(data=prompt_cases)\n for k in res:\n print(f\"{k}: {res[k]}\")\n print(\"\")\n\n if args.dataset_name == \"CLUTRR\":\n prompt_cases = post_process_answer_clutrr_cutoff(prompt_cases)\n # print the random top1 and recall of the data\n print(\"*********** Data statistics (after post processing for CLUTRR) ***********\")\n res = compute_top1_and_recall(data=prompt_cases)\n for k in res:\n print(f\"{k}: {res[k]}\")\n print(\"\")\n\n # Step-wise Labeling\n for j, case in enumerate(tqdm(prompt_cases)):\n case.do_step_labeling(model=model, tokenizer=tokenizer)\n \n # pdb.set_trace()\n \n for case_idx, case in enumerate(tqdm(prompt_cases)):\n case.ground_truth.sequence_labels = example_class_map[args.dataset_name].get_sequence_labels(case.question, case.ground_truth) \n for pred_idx, pred in enumerate(case.preds):\n pred.sequence_labels = example_class_map[args.dataset_name].get_sequence_labels(case.question, pred)\n # pdb.set_trace()\n # pdb.set_trace()\n \n sequence_data = []\n for case_idx, case in enumerate(tqdm(prompt_cases)):\n sequence_data.append(case.ground_truth.sequence_labels)\n for pred_idx, pred in enumerate(case.preds):\n sequence_data.append(pred.sequence_labels)\n # pdb.set_trace()\n\n # Train file is shuffled, but test file is not\n if args.split == \"train\":\n random.shuffle(sequence_data)\n \n with open(os.path.join(args.output_dir, '{}.txt'.format(args.split)), \"w\") as f:\n for i, arr in enumerate(tqdm(sequence_data)):\n for lhs, rhs in arr:\n f.write(f\"{lhs} {rhs}\\n\")\n f.write(\"\\n\")\n\nif __name__ == '__main__':\n main()","source_hash":"955b9f68d4841759bf1c3467ad43446e738d69fb4db84eec80d086ec766cc7c3","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.verifier_data_prepare.main","uri":"program://CodeT/function/DIVERSE.code.src.verifier_data_prepare.main#L65-L224","kind":"function","name":"main","path":"DIVERSE/code/src/verifier_data_prepare.py","language":"python","start_line":65,"end_line":224,"context_start_line":45,"context_end_line":227,"code":" 'father': ['son', 'daughter'],\n 'grandfather': ['grandson', 'granddaughter'],\n 'grandmother': ['grandson', 'granddaughter'],\n 'mother-in-law': ['son-in-law', 'daughter-in-law'],\n 'uncle': ['nephew', 'niece'],\n 'niece': ['uncle', 'aunt'],\n 'mother': ['son', 'daughter'],\n 'brother': ['sister'],\n 'daughter': ['father', 'mother'],\n 'nephew': ['uncle', 'aunt'],\n 'grandson': ['grandfather', 'grandmother'],\n 'son-in-law': ['father-in-law', 'mother-in-law'],\n 'father-in-law': ['son-in-law', 'daughter-in-law'],\n 'daughter-in-law': ['father-in-law', 'mother-in-law'],\n}\n\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n# device = \"cpu\"\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--generator_result_file\", type=str, default=None, help=\"generator output file in .jsonl format\")\n parser.add_argument(\"--output_dir\", type=str, default=None, help=\"output dir\")\n parser.add_argument(\"--random_seed\", type=int, default=233, help=\"random_seed\")\n parser.add_argument(\"--split\", type=str, default=\"train\", help=\"split (train or test)\")\n parser.add_argument(\"--dataset_name\", type=str, default=\"GSM8K\", help=\"GSM8K, CLUTRR, strategyQA\")\n parser.add_argument(\"--text_entailment_model_name\", type=str, default=\"roberta-large-mnli\", help=\"roberta-large-mnli, facebook/bart-large-mnli, etc.\")\n parser.add_argument(\"--text_entailment_batch_size\", type=int, default=512, help=\"text entailment batch size\")\n args = parser.parse_args()\n\n random.seed(args.random_seed)\n\n if args.dataset_name != \"GSM8K\":\n logger.info(\"Loading textual entailment models...\")\n model = AutoModelForSequenceClassification.from_pretrained(args.text_entailment_model_name).to(device)\n model.eval()\n tokenizer = AutoTokenizer.from_pretrained(args.text_entailment_model_name)\n else:\n model = None\n tokenizer = None\n \n # loading data from generator output result file\n generator_outputs = [json.loads(line) for line in open(utils_io.get_file(args.generator_result_file))]\n question_to_ground_truth = {}\n\n # prompt data make up\n prompt_data = []\n for generator_output in generator_outputs:\n context = generator_output[\"context\"]\n samples = generator_output[\"samples\"]\n for sample in samples:\n metadata = generator_output[\"metadata\"]\n prompt_data.append({\"context\": context, \"sample\": sample, \"metadata\": metadata})\n\n prompt_data_dict = {}\n\n # some pre-processing about formulas and answers for GSM8K and other datasets\n for obj in tqdm(prompt_data):\n question = obj[\"metadata\"][\"question\"].strip().replace(\"\\n\", \"\")\n def extract_solution(sample):\n sample = sample.strip()\n if '####' in sample:\n stop = sample.find('\\n\\n', sample.index('####'))\n if stop >= 0:\n sample = sample[:stop]\n sample = sample.replace('\\n\\n', '\\n')\n return sample\n sample = extract_solution(obj[\"sample\"])\n sample = sample.strip().replace(\"\\n\", \"%%\") # for sequence labeling\n ground_truth = obj[\"metadata\"][\"ground_truth\"].strip().replace(\"\\n\\n\", \"\\n\").replace(\"\\n\", \"%%\") # for sequence labeling\n if args.dataset_name == \"GSM8K\":\n if \"####\" not in sample:\n reg = \"<<.+>>[\\d\\.]+\"\n eqs = re.findall(reg, sample)\n if len(eqs) > 0:\n final_answer = eqs[-1].split(\">>\")[-1].strip()\n if final_answer and len(final_answer) > 0 and final_answer[-1] == '.':\n final_answer = final_answer[:-1]\n if sample[-2:] == \"%%\":\n sample = sample + \"####\" + final_answer\n else:\n sample = sample + \"%%####\" + final_answer\n elif args.dataset_name == \"CLUTRR\":\n pass\n if \"####\" not in sample:\n reg = \"the.+?of\"\n eqs = re.findall(reg, sample)\n if len(eqs) > 0:\n final_answer = eqs[-1].replace(\"the \", \"\").replace(\" of\", \"\")\n if sample[-2:] == \"%%\":\n sample = sample + \"####\" + final_answer\n else:\n sample = sample + \"%%####\" + final_answer\n if question not in prompt_data_dict:\n prompt_data_dict[question] = []\n \n sample = sample.replace(\"\\n\", \"%%\") # for sequence labeling\n ground_truth = ground_truth.replace(\"\\n\", \"%%\") # for sequence labeling\n question_to_ground_truth[question] = ground_truth\n prompt_data_dict[question].append(sample)\n\n # # code change\n # if args.dataset_name == \"CLUTRR\":\n # if \"####\" not in sample:\n # continue\n # sample_body, sample_answer = sample.split(\"####\")[0].strip(), sample.split(\"####\")[-1].strip()\n # # pdb.set_trace()\n # if sample_answer in relation_reverse_map:\n # for reverse in relation_reverse_map[sample_answer]:\n # prompt_data_dict[question].append(sample_body + \"####\" + reverse)\n \n # check the least sample num among all the cases\n min_sample_num_per_case = 99999999\n for k in prompt_data_dict:\n min_sample_num_per_case = min(min_sample_num_per_case, len(prompt_data_dict[k]))\n\n # converting data into Case\n prompt_cases = []\n for k in prompt_data_dict:\n case = case_class_map[args.dataset_name](\"\", [])\n case.question = k\n case.ground_truth = example_class_map[args.dataset_name](question_to_ground_truth[k])\n case.entailment_batch_size = args.text_entailment_batch_size\n for sample_idx, x in enumerate(prompt_data_dict[k]):\n if sample_idx >= min_sample_num_per_case:\n break\n pred = example_class_map[args.dataset_name](x)\n case.preds.append(pred)\n prompt_cases.append(case)\n print(f\"Total cases: {len(prompt_cases)}\".replace(\"\\n\", \"\\\\n\"))\n print(f\"Case 0's question: {prompt_cases[0].question}\".replace(\"\\n\", \"\\\\n\"))\n print(f\"Case 0's ground truth: {prompt_cases[0].ground_truth.content}\".replace(\"\\n\", \"\\\\n\"))\n print(f\"Case 0's sample0: {prompt_cases[0].preds[0].content}\".replace(\"\\n\", \"\\\\n\"))\n\n # print the random top1 and recall of the data\n print(\"*********** Data statistics ***********\")\n res = compute_top1_and_recall(data=prompt_cases)\n for k in res:\n print(f\"{k}: {res[k]}\")\n print(\"\")\n\n if args.dataset_name == \"CLUTRR\":\n prompt_cases = post_process_answer_clutrr_cutoff(prompt_cases)\n # print the random top1 and recall of the data\n print(\"*********** Data statistics (after post processing for CLUTRR) ***********\")\n res = compute_top1_and_recall(data=prompt_cases)\n for k in res:\n print(f\"{k}: {res[k]}\")\n print(\"\")\n\n # Step-wise Labeling\n for j, case in enumerate(tqdm(prompt_cases)):\n case.do_step_labeling(model=model, tokenizer=tokenizer)\n \n # pdb.set_trace()\n \n for case_idx, case in enumerate(tqdm(prompt_cases)):\n case.ground_truth.sequence_labels = example_class_map[args.dataset_name].get_sequence_labels(case.question, case.ground_truth) \n for pred_idx, pred in enumerate(case.preds):\n pred.sequence_labels = example_class_map[args.dataset_name].get_sequence_labels(case.question, pred)\n # pdb.set_trace()\n # pdb.set_trace()\n \n sequence_data = []\n for case_idx, case in enumerate(tqdm(prompt_cases)):\n sequence_data.append(case.ground_truth.sequence_labels)\n for pred_idx, pred in enumerate(case.preds):\n sequence_data.append(pred.sequence_labels)\n # pdb.set_trace()\n\n # Train file is shuffled, but test file is not\n if args.split == \"train\":\n random.shuffle(sequence_data)\n \n with open(os.path.join(args.output_dir, '{}.txt'.format(args.split)), \"w\") as f:\n for i, arr in enumerate(tqdm(sequence_data)):\n for lhs, rhs in arr:\n f.write(f\"{lhs} {rhs}\\n\")\n f.write(\"\\n\")\n\nif __name__ == '__main__':\n main()","source_hash":"955b9f68d4841759bf1c3467ad43446e738d69fb4db84eec80d086ec766cc7c3","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.verifier_data_prepare.extract_solution","uri":"program://CodeT/function/DIVERSE.code.src.verifier_data_prepare.extract_solution#L105-L112","kind":"function","name":"extract_solution","path":"DIVERSE/code/src/verifier_data_prepare.py","language":"python","start_line":105,"end_line":112,"context_start_line":85,"context_end_line":132,"code":" tokenizer = None\n \n # loading data from generator output result file\n generator_outputs = [json.loads(line) for line in open(utils_io.get_file(args.generator_result_file))]\n question_to_ground_truth = {}\n\n # prompt data make up\n prompt_data = []\n for generator_output in generator_outputs:\n context = generator_output[\"context\"]\n samples = generator_output[\"samples\"]\n for sample in samples:\n metadata = generator_output[\"metadata\"]\n prompt_data.append({\"context\": context, \"sample\": sample, \"metadata\": metadata})\n\n prompt_data_dict = {}\n\n # some pre-processing about formulas and answers for GSM8K and other datasets\n for obj in tqdm(prompt_data):\n question = obj[\"metadata\"][\"question\"].strip().replace(\"\\n\", \"\")\n def extract_solution(sample):\n sample = sample.strip()\n if '####' in sample:\n stop = sample.find('\\n\\n', sample.index('####'))\n if stop >= 0:\n sample = sample[:stop]\n sample = sample.replace('\\n\\n', '\\n')\n return sample\n sample = extract_solution(obj[\"sample\"])\n sample = sample.strip().replace(\"\\n\", \"%%\") # for sequence labeling\n ground_truth = obj[\"metadata\"][\"ground_truth\"].strip().replace(\"\\n\\n\", \"\\n\").replace(\"\\n\", \"%%\") # for sequence labeling\n if args.dataset_name == \"GSM8K\":\n if \"####\" not in sample:\n reg = \"<<.+>>[\\d\\.]+\"\n eqs = re.findall(reg, sample)\n if len(eqs) > 0:\n final_answer = eqs[-1].split(\">>\")[-1].strip()\n if final_answer and len(final_answer) > 0 and final_answer[-1] == '.':\n final_answer = final_answer[:-1]\n if sample[-2:] == \"%%\":\n sample = sample + \"####\" + final_answer\n else:\n sample = sample + \"%%####\" + final_answer\n elif args.dataset_name == \"CLUTRR\":\n pass\n if \"####\" not in sample:\n reg = \"the.+?of\"\n eqs = re.findall(reg, sample)","source_hash":"955b9f68d4841759bf1c3467ad43446e738d69fb4db84eec80d086ec766cc7c3","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils","uri":"program://CodeT/module/DIVERSE.code.src.utils#L1-L513","kind":"module","name":"DIVERSE.code.src.utils","path":"DIVERSE/code/src/utils.py","language":"python","start_line":1,"end_line":513,"context_start_line":1,"context_end_line":513,"code":"import re\nfrom tqdm import tqdm\nfrom multiset import Multiset\nfrom functools import lru_cache\nimport random\nimport json\nimport pdb\nimport torch\nimport torch.nn.functional as F\nimport numpy as np\nfrom transformers import (\n AutoModelForSequenceClassification,\n AutoTokenizer,\n pipeline,\n)\nimport time\n\n\nclass BaseCase:\n def __init__(self, ground_truth, preds):\n self.question = \"\"\n self.ground_truth = ground_truth\n self.preds = preds\n self.correct_preds_num = 0.0\n\n\nclass GSM8KCase(BaseCase):\n def __init__(self, ground_truth, preds):\n super().__init__(ground_truth, preds)\n self.entailment_batch_size = 512\n\n def do_step_labeling(self, model=None, tokenizer=None):\n # 将ground_truth标记为true\n self.ground_truth.is_correct = True\n for step in self.ground_truth.steps:\n self.ground_truth.step_labels[step] = 1\n\n # 先预存正样本集合\n positive_preds = [self.ground_truth]\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n positive_preds.append(pred)\n\n # 再对所有样本的所有step打标签\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n pred.is_correct = True\n for step in pred.steps:\n pred.step_labels[step] = 1\n else:\n for k, step in enumerate(pred.steps):\n ans = GSM8KExample.match(\n pred.steps[:k+1],\n positive_preds,\n model=model,\n tokenizer=tokenizer,\n )\n pred.step_labels[step] = ans\n\n\nclass TextEntailmentCase(BaseCase):\n def __init__(self, ground_truth, preds, entailment_batch_size=512):\n super().__init__(ground_truth, preds)\n self.entailment_results = {}\n self.entailment_batch_size = entailment_batch_size\n\n def do_step_labeling(self, model=None, tokenizer=None):\n # 将ground_truth标记为true\n self.ground_truth.is_correct = True\n for step in self.ground_truth.steps:\n self.ground_truth.step_labels[step] = 1\n\n # 先预存正样本集合\n positive_preds = [self.ground_truth]\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n positive_preds.append(pred)\n\n # 将所有待NLI的文本预存起来\n self.collect_entailment_texts(positive_preds)\n\n # print(\"Number of entailment result keys:\", len(self.entailment_results.keys()))\n\n # 预处理所有NLI结果\n self.preprocess_entailment(model=model, tokenizer=tokenizer)\n\n # 再对所有样本的所有step打标签\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n pred.is_correct = True\n for step in pred.steps:\n pred.step_labels[step] = 1\n else:\n for k, step in enumerate(pred.steps):\n ans = TextEntailmentExample.match(\n pred.steps[:k+1],\n positive_preds,\n model=model,\n tokenizer=tokenizer,\n entailment_result_dict=self.entailment_results,\n )\n pred.step_labels[step] = ans\n \n def collect_entailment_texts(self, positive_preds):\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n pass\n else:\n for pp in positive_preds:\n for k, step in enumerate(pred.steps):\n if k >= len(pp.steps):\n continue\n pp_step = pp.steps[k].strip()\n text1 = f\"premise: {pp_step} hypothesis: {step}\"\n text2 = f\"premise: {step} hypothesis: {pp_step}\"\n self.entailment_results[text1] = -1\n self.entailment_results[text2] = -1\n \n def preprocess_entailment(self, model, tokenizer):\n text_all = list(self.entailment_results.keys())\n text_batch, results_batch = [], []\n for i in range(0, len(text_all), self.entailment_batch_size):\n text_batch = text_all[i : min(len(text_all), i + self.entailment_batch_size)]\n batch_results = entailment_batch(text_batch, model, tokenizer)\n for sc in batch_results:\n results_batch.append(sc)\n for text, result in zip(text_batch, results_batch):\n self.entailment_results[text] = 1 if result else 0\n\n\nclass BaseExample:\n inf = \"-99999999\"\n \n def __init__(self, content):\n self.content = content.strip()\n self.steps = self.get_steps()\n self.step_labels = {}\n self.sequence_labels = []\n self.is_correct= False\n\n # Only for GSM8K dataset use\n def init_equations(self):\n raise NotImplementedError\n\n def get_steps(self):\n return [x+\"%%\" if x != self.content.split(\"%%\")[-1] else x for i, x in enumerate(self.content.split(\"%%\"))]\n\n def get_final_answer(self):\n ans = \"\"\n if \"####\" in self.content:\n ans = self.content.split(\"####\")[-1].strip().replace(\"%%\", \"\").replace(\" \", \"\")\n else:\n ans = BaseExample.inf\n return clean_ans(ans)\n\n def label_to_string(self):\n return \"\".join(str(self.labels[k]) for k in self.labels.keys())\n\n\nclass GSM8KExample(BaseExample):\n def __init__(self, content):\n super().__init__(content)\n self.equations = self.init_equations()\n self.verifier_score = 0.0\n\n # 按'<>'的格式将公式提取出来\n def init_equations(self):\n return [x for x in re.findall(\"<<.+>>[0-9\\.]+\", self.content) if \"=\" in x]\n\n def get_step_answer(step):\n expression = re.findall(\"<<.+>>[0-9\\.]+\", step)\n if len(expression == 0):\n ans = BaseExample.inf\n else:\n ans = expression[-1].split(\">>\")[-1].strip()\n return clean_ans(ans)\n \n @staticmethod\n @lru_cache(maxsize=4096)\n def get_answer(s):\n ans = \"\"\n if \"####\" in s:\n ans = s.split(\"####\")[-1].replace(\"%%\", \"\").replace(\" \", \"\").strip()\n else:\n expression = re.findall(\"<<.+>>[0-9\\.]+\", s)\n if len(expression) == 0:\n ans = GSM8KExample.inf\n else:\n ans = expression[-1].split(\">>\")[-1].strip()\n return clean_ans(ans)\n \n @staticmethod\n def match(steps, positive_examples, model=None, tokenizer=None):\n curr_set = Multiset([GSM8KExample.get_answer(x) for x in steps])\n for positive_example in positive_examples:\n golden_set = Multiset([GSM8KExample.get_answer(x) for x in positive_example.steps])\n if GSM8KExample.inf in curr_set:\n curr_set.remove(GSM8KExample.inf)\n if GSM8KExample.inf in golden_set:\n golden_set.remove(GSM8KExample.inf)\n if len(curr_set) == 0:\n return 0\n if curr_set.issubset(golden_set):\n return 1\n return 0\n \n def get_sequence_labels(question, pred):\n sequence_labels = []\n if pred.is_correct:\n sequence_labels.append((\"[CLS]\", \"SOLUTION-CORRECT\"))\n else:\n sequence_labels.append((\"[CLS]\", \"SOLUTION-INCORRECT\"))\n\n # add step tokens\n for s in pred.steps:\n token_list = [x for x in re.split(\"(>>| )\", s) if x != ' ']\n for token in token_list:\n if token == \">>\":\n if pred.step_labels[s] == 1:\n sequence_labels.append((token, \"STEP-CORRECT\"))\n else:\n sequence_labels.append((token, \"STEP-INCORRECT\"))\n else:\n sequence_labels.append((token, \"O\"))\n\n # add a split symbol\n sequence_labels.append((\"&&\", \"O\"))\n\n # add question tokens\n for token in question.split(\" \"):\n sequence_labels.append((token, \"O\"))\n\n return sequence_labels\n \n\nclass TextEntailmentExample(BaseExample):\n def __init__(self, content):\n super().__init__(content)\n\n @staticmethod\n def match(steps, positive_examples, model, tokenizer, entailment_result_dict):\n for pp in positive_examples:\n if TextEntailmentExample.match_per_example(pp, steps, entailment_result_dict):\n return 1\n return 0\n \n @staticmethod\n def match_per_example(pp, steps, entailment_result_dict):\n for k, step in enumerate(steps):\n if k >= len(pp.steps):\n continue\n # print(\"step:\", step)\n # print(\"pp.steps[k]:\", pp.steps[k])\n pp_step = pp.steps[k].strip()\n text1 = f\"premise: {step} hypothesis: {pp_step}\"\n text2 = f\"premise: {pp_step} hypothesis: {step}\"\n if entailment_result_dict[text1] == 0 or entailment_result_dict[text2] == 0:\n # error_case = 'No, Christmas trees are not dissimilar to deciduous trees.%%Both Christmas trees and deciduous trees are types of trees.%%Both Christmas trees and deciduous trees have leaves.%%So the answer is no.#### no'\n # if error_case in text1 or error_case in text2:\n # print(\"text1:\", text1)\n # print(\"text2:\", text2)\n # pdb.set_trace()\n return 0\n return 1\n\n def get_sequence_labels(question, pred):\n sequence_labels = []\n if pred.is_correct:\n sequence_labels.append((\"[CLS]\", \"SOLUTION-CORRECT\"))\n else:\n sequence_labels.append((\"[CLS]\", \"SOLUTION-INCORRECT\"))\n\n # add step tokens\n for s in pred.steps:\n token_list = [x for x in re.split(\"(%%| )\", s) if x != ' ']\n for token in token_list:\n if token == \"\":\n continue\n if token == \"%%\":\n if pred.step_labels[s] == 1:\n sequence_labels.append((token, \"STEP-CORRECT\"))\n else:\n sequence_labels.append((token, \"STEP-INCORRECT\"))\n else:\n sequence_labels.append((token, \"O\"))\n\n # add a split symbol\n sequence_labels.append((\"&&\", \"O\"))\n\n # add question tokens\n for token in question.split(\" \"):\n sequence_labels.append((token, \"O\"))\n\n return sequence_labels\n\n\n@torch.no_grad()\ndef entailment_batch(text, model, tokenizer):\n inputs = tokenizer(text, padding=True, truncation=True, return_tensors=\"pt\").to(\"cuda\")\n labels = torch.tensor([1] * len(text)).to(\"cuda\")\n outputs = model(**inputs, labels=labels)\n logits = outputs.logits\n ans_list = torch.argmax(F.softmax(logits, dim=-1), dim=-1).tolist()\n ans_list = [x == model.config.label2id[\"ENTAILMENT\"] for x in ans_list]\n return ans_list\n\n\n@torch.no_grad()\ndef entailment(premise, hypothesis, model, tokenizer):\n text = f\"premise: {premise} hypothesis: {hypothesis}\"\n inputs = tokenizer(text, padding=True, truncation=True, return_tensors=\"pt\").to(model.device)\n labels = torch.tensor([1]).to(model.device)\n outputs = model(**inputs, labels=labels)\n logits = outputs.logits\n ans = torch.argmax(F.softmax(logits, dim=-1)).item() == model.config.label2id[\"ENTAILMENT\"]\n return ans\n\n\ndef convert_eval_sequences_to_cases(eval_sequences, pred_num_per_case, case_class, example_class):\n cases = []\n for i in range(0, len(eval_sequences), pred_num_per_case + 1):\n case = case_class(\"\", [])\n # question, grount_truth = eval_sequences[i].split(\"&&\")[0], eval_sequences[i].split(\"&&\")[1]\n question, grount_truth = eval_sequences[i].split(\"&&\")[1], eval_sequences[i].split(\"&&\")[0]\n case.ground_truth = example_class(grount_truth)\n case.question = question\n for j in range(i+1, i+pred_num_per_case+1):\n # case.preds.append(GSM8KExample(eval_sequences[j].split(\"&&\")[1]))\n case.preds.append(example_class(eval_sequences[j].split(\"&&\")[0]))\n cases.append(case)\n # if example_class.__name__ == \"TextEntailmentExample\":\n # cases = post_process_answer_clutrr(cases)\n return cases\n\n\ndef post_process_answer_clutrr_mapping(cases):\n print(\"before loading pipeline\")\n classifier = pipeline(\"zero-shot-classification\", device=0)\n print(\"after loading pipeline\")\n print(\"post processing\")\n candidate_labels = ['sister', 'son', 'aunt', 'granddaughter', 'father', 'grandfather', 'grandmother', 'mother-in-law', 'uncle', 'niece', 'mother', 'brother', 'daughter', 'nephew', 'grandson', 'son-in-law', 'father-in-law', 'daughter-in-law']\n for case_idx, case in tqdm(enumerate(cases)):\n gt_ans = case.ground_truth.get_final_answer()\n # skip StrategyQA task\n if gt_ans == \"yes\" or gt_ans == \"no\":\n break\n for pred in case.preds:\n pred_ans = pred.get_final_answer()\n if pred_ans != BaseExample.inf and pred_ans != gt_ans:\n outputs = classifier(pred_ans, candidate_labels)\n logits = outputs[\"scores\"]\n labels = outputs[\"labels\"]\n candidate_index = np.argmax(logits)\n most_similar_answer = labels[candidate_index]\n body = pred.content.split(\"####\")[0]\n pred.content = body + \"####\" + most_similar_answer\n # pdb.set_trace()\n return cases\n \n\ndef post_process_answer_clutrr_cutoff(cases):\n candidate_labels = ['sister', 'son', 'aunt', 'granddaughter', 'father', 'grandfather', 'grandmother', 'mother-in-law', 'uncle', 'niece', 'mother', 'brother', 'daughter', 'nephew', 'grandson', 'son-in-law', 'father-in-law', 'daughter-in-law']\n for case_idx, case in tqdm(enumerate(cases)):\n gt_ans = case.ground_truth.get_final_answer()\n # skip StrategyQA task\n if gt_ans == \"yes\" or gt_ans == \"no\":\n break\n for pred in case.preds:\n pred_ans = pred.get_final_answer()\n if pred_ans not in candidate_labels:\n body = pred.content.split(\"####\")[0]\n pred.content = body + \"####\" + BaseExample.inf\n return cases\n\n\ndef random_1_hit(gt_ans, preds):\n idx = random.randint(0, len(preds)-1)\n # random 1 acc\n pred0_ans = preds[idx].get_final_answer()\n return 1 if pred0_ans == gt_ans else 0\n\n\ndef recall_hit(gt_ans, preds):\n for pred in preds:\n if pred.get_final_answer() == gt_ans:\n return 1\n return 0\n\n\ndef voting_hit(gt_ans, preds):\n # voting acc\n answers = {}\n for pred in preds:\n if pred.get_final_answer() not in answers:\n answers[pred.get_final_answer()] = 0\n answers[pred.get_final_answer()] += 1\n answers = sorted(answers.items(), key=lambda x : x[1], reverse=True)\n for i in range(len(answers)):\n ans, ans_cnt = answers[i][0], answers[i][1]\n if ans != GSM8KExample.inf:\n return 1 if ans == gt_ans else 0\n return 0\n\n\ndef weighted_voting_hit(gt_ans, preds):\n # voting acc\n answers = {}\n for pred in preds:\n if pred.get_final_answer() not in answers:\n answers[pred.get_final_answer()] = 0\n answers[pred.get_final_answer()] += pred.verifier_score\n answers = sorted(answers.items(), key=lambda x : x[1], reverse=True)\n for i in range(len(answers)):\n ans, ans_cnt = answers[i][0], answers[i][1]\n if ans != GSM8KExample.inf:\n return 1 if ans == gt_ans else 0\n return 0\n\n\ndef verification_hit(gt_ans, preds):\n preds = sorted(preds, key=lambda x : x.verifier_score, reverse=True)\n for pred in preds:\n ans = pred.get_final_answer()\n if ans != GSM8KExample.inf:\n return 1 if ans == gt_ans else 0\n return 0\n\n\ndef compute_top1_and_recall(data, rand_k=100):\n total_random_hit_cnt = 0\n total_vote_cnt = 0\n total_recall_cnt = 0\n for i, x in enumerate(data):\n gt_ans = x.ground_truth.get_final_answer()\n slice = x.preds if rand_k >= len(x.preds) else random.sample(x.preds, rand_k)\n \n total_random_hit_cnt += random_1_hit(gt_ans, slice)\n total_vote_cnt += voting_hit(gt_ans, slice)\n total_recall_cnt += recall_hit(gt_ans, slice)\n result = {\n \"random_top1\": total_random_hit_cnt / len(data), \n \"voting_top1_accuracy\": total_vote_cnt / len(data),\n \"recall\": total_recall_cnt / len(data),\n }\n return result\n\n\ndef compute_results(data, rand_k=100):\n total_random_hit_cnt = 0\n total_recall_cnt = 0\n total_vote_cnt = 0\n total_weighted_vote_cnt = 0\n total_verification_cnt = 0\n for i, x in enumerate(data):\n gt_ans = x.ground_truth.get_final_answer()\n slice = x.preds if rand_k == len(x.preds) else random.sample(x.preds, rand_k)\n \n total_random_hit_cnt += random_1_hit(gt_ans, slice)\n total_vote_cnt += voting_hit(gt_ans, slice)\n total_recall_cnt += recall_hit(gt_ans, slice)\n total_weighted_vote_cnt += weighted_voting_hit(gt_ans, slice)\n total_verification_cnt += verification_hit(gt_ans, slice)\n result = {\n \"random_top1\": total_random_hit_cnt / len(data), \n f\"recall@{rand_k}\": total_recall_cnt / len(data),\n f\"verifier_top1_accuracy@{rand_k}\": total_verification_cnt / len(data),\n f\"voting_top1_accuracy@{rand_k}\": total_vote_cnt / len(data),\n f\"weighted_voting_top1_accuracy@{rand_k}\": total_weighted_vote_cnt / len(data),\n }\n return result\n\n\ndef compute_results_avg(data, rand_k=100, repeat_time=5):\n sum_result_dict = {\n \"random_top1\": 0, \n f\"recall@{rand_k}\": 0,\n f\"verifier_top1_accuracy@{rand_k}\": 0,\n f\"voting_top1_accuracy@{rand_k}\": 0,\n f\"weighted_voting_top1_accuracy@{rand_k}\": 0,\n }\n for i in tqdm(range(repeat_time)):\n for k in sum_result_dict:\n result_dict = compute_results(data, rand_k=rand_k)\n sum_result_dict[k] += result_dict[k]\n for k in sum_result_dict:\n sum_result_dict[k] = sum_result_dict[k] / repeat_time if repeat_time != 1 else sum_result_dict[k]\n sum_result_dict[k] = round(sum_result_dict[k], 8)\n return sum_result_dict\n \n\ndef dedup(li):\n s = set()\n new_li = []\n for x in li:\n if str(x) not in s:\n new_li.append(x)\n s.add(str(x))\n return new_li\n\n\ndef print_stat(data):\n cnt = 0\n for x in data:\n if x[\"output\"] == \"correct\":\n cnt += 1\n print(cnt, len(data) - cnt, len(data))\n\n\ndef clean_ans(s):\n s = str(s)\n if s and len(s) > 0 and s[-1] == '.':\n s = s[:-1]\n return s.lower() # for CLUTRR and strategyQA use","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.BaseCase","uri":"program://CodeT/class/DIVERSE.code.src.utils.BaseCase#L19-L24","kind":"class","name":"BaseCase","path":"DIVERSE/code/src/utils.py","language":"python","start_line":19,"end_line":24,"context_start_line":1,"context_end_line":44,"code":"import re\nfrom tqdm import tqdm\nfrom multiset import Multiset\nfrom functools import lru_cache\nimport random\nimport json\nimport pdb\nimport torch\nimport torch.nn.functional as F\nimport numpy as np\nfrom transformers import (\n AutoModelForSequenceClassification,\n AutoTokenizer,\n pipeline,\n)\nimport time\n\n\nclass BaseCase:\n def __init__(self, ground_truth, preds):\n self.question = \"\"\n self.ground_truth = ground_truth\n self.preds = preds\n self.correct_preds_num = 0.0\n\n\nclass GSM8KCase(BaseCase):\n def __init__(self, ground_truth, preds):\n super().__init__(ground_truth, preds)\n self.entailment_batch_size = 512\n\n def do_step_labeling(self, model=None, tokenizer=None):\n # 将ground_truth标记为true\n self.ground_truth.is_correct = True\n for step in self.ground_truth.steps:\n self.ground_truth.step_labels[step] = 1\n\n # 先预存正样本集合\n positive_preds = [self.ground_truth]\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n positive_preds.append(pred)\n\n # 再对所有样本的所有step打标签","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.GSM8KCase","uri":"program://CodeT/class/DIVERSE.code.src.utils.GSM8KCase#L27-L58","kind":"class","name":"GSM8KCase","path":"DIVERSE/code/src/utils.py","language":"python","start_line":27,"end_line":58,"context_start_line":7,"context_end_line":78,"code":"import pdb\nimport torch\nimport torch.nn.functional as F\nimport numpy as np\nfrom transformers import (\n AutoModelForSequenceClassification,\n AutoTokenizer,\n pipeline,\n)\nimport time\n\n\nclass BaseCase:\n def __init__(self, ground_truth, preds):\n self.question = \"\"\n self.ground_truth = ground_truth\n self.preds = preds\n self.correct_preds_num = 0.0\n\n\nclass GSM8KCase(BaseCase):\n def __init__(self, ground_truth, preds):\n super().__init__(ground_truth, preds)\n self.entailment_batch_size = 512\n\n def do_step_labeling(self, model=None, tokenizer=None):\n # 将ground_truth标记为true\n self.ground_truth.is_correct = True\n for step in self.ground_truth.steps:\n self.ground_truth.step_labels[step] = 1\n\n # 先预存正样本集合\n positive_preds = [self.ground_truth]\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n positive_preds.append(pred)\n\n # 再对所有样本的所有step打标签\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n pred.is_correct = True\n for step in pred.steps:\n pred.step_labels[step] = 1\n else:\n for k, step in enumerate(pred.steps):\n ans = GSM8KExample.match(\n pred.steps[:k+1],\n positive_preds,\n model=model,\n tokenizer=tokenizer,\n )\n pred.step_labels[step] = ans\n\n\nclass TextEntailmentCase(BaseCase):\n def __init__(self, ground_truth, preds, entailment_batch_size=512):\n super().__init__(ground_truth, preds)\n self.entailment_results = {}\n self.entailment_batch_size = entailment_batch_size\n\n def do_step_labeling(self, model=None, tokenizer=None):\n # 将ground_truth标记为true\n self.ground_truth.is_correct = True\n for step in self.ground_truth.steps:\n self.ground_truth.step_labels[step] = 1\n\n # 先预存正样本集合\n positive_preds = [self.ground_truth]\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n positive_preds.append(pred)\n","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.TextEntailmentCase","uri":"program://CodeT/class/DIVERSE.code.src.utils.TextEntailmentCase#L61-L128","kind":"class","name":"TextEntailmentCase","path":"DIVERSE/code/src/utils.py","language":"python","start_line":61,"end_line":128,"context_start_line":41,"context_end_line":148,"code":" if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n positive_preds.append(pred)\n\n # 再对所有样本的所有step打标签\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n pred.is_correct = True\n for step in pred.steps:\n pred.step_labels[step] = 1\n else:\n for k, step in enumerate(pred.steps):\n ans = GSM8KExample.match(\n pred.steps[:k+1],\n positive_preds,\n model=model,\n tokenizer=tokenizer,\n )\n pred.step_labels[step] = ans\n\n\nclass TextEntailmentCase(BaseCase):\n def __init__(self, ground_truth, preds, entailment_batch_size=512):\n super().__init__(ground_truth, preds)\n self.entailment_results = {}\n self.entailment_batch_size = entailment_batch_size\n\n def do_step_labeling(self, model=None, tokenizer=None):\n # 将ground_truth标记为true\n self.ground_truth.is_correct = True\n for step in self.ground_truth.steps:\n self.ground_truth.step_labels[step] = 1\n\n # 先预存正样本集合\n positive_preds = [self.ground_truth]\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n positive_preds.append(pred)\n\n # 将所有待NLI的文本预存起来\n self.collect_entailment_texts(positive_preds)\n\n # print(\"Number of entailment result keys:\", len(self.entailment_results.keys()))\n\n # 预处理所有NLI结果\n self.preprocess_entailment(model=model, tokenizer=tokenizer)\n\n # 再对所有样本的所有step打标签\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n pred.is_correct = True\n for step in pred.steps:\n pred.step_labels[step] = 1\n else:\n for k, step in enumerate(pred.steps):\n ans = TextEntailmentExample.match(\n pred.steps[:k+1],\n positive_preds,\n model=model,\n tokenizer=tokenizer,\n entailment_result_dict=self.entailment_results,\n )\n pred.step_labels[step] = ans\n \n def collect_entailment_texts(self, positive_preds):\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n pass\n else:\n for pp in positive_preds:\n for k, step in enumerate(pred.steps):\n if k >= len(pp.steps):\n continue\n pp_step = pp.steps[k].strip()\n text1 = f\"premise: {pp_step} hypothesis: {step}\"\n text2 = f\"premise: {step} hypothesis: {pp_step}\"\n self.entailment_results[text1] = -1\n self.entailment_results[text2] = -1\n \n def preprocess_entailment(self, model, tokenizer):\n text_all = list(self.entailment_results.keys())\n text_batch, results_batch = [], []\n for i in range(0, len(text_all), self.entailment_batch_size):\n text_batch = text_all[i : min(len(text_all), i + self.entailment_batch_size)]\n batch_results = entailment_batch(text_batch, model, tokenizer)\n for sc in batch_results:\n results_batch.append(sc)\n for text, result in zip(text_batch, results_batch):\n self.entailment_results[text] = 1 if result else 0\n\n\nclass BaseExample:\n inf = \"-99999999\"\n \n def __init__(self, content):\n self.content = content.strip()\n self.steps = self.get_steps()\n self.step_labels = {}\n self.sequence_labels = []\n self.is_correct= False\n\n # Only for GSM8K dataset use\n def init_equations(self):\n raise NotImplementedError\n\n def get_steps(self):\n return [x+\"%%\" if x != self.content.split(\"%%\")[-1] else x for i, x in enumerate(self.content.split(\"%%\"))]\n\n def get_final_answer(self):","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.BaseExample","uri":"program://CodeT/class/DIVERSE.code.src.utils.BaseExample#L131-L157","kind":"class","name":"BaseExample","path":"DIVERSE/code/src/utils.py","language":"python","start_line":131,"end_line":157,"context_start_line":111,"context_end_line":177,"code":" if k >= len(pp.steps):\n continue\n pp_step = pp.steps[k].strip()\n text1 = f\"premise: {pp_step} hypothesis: {step}\"\n text2 = f\"premise: {step} hypothesis: {pp_step}\"\n self.entailment_results[text1] = -1\n self.entailment_results[text2] = -1\n \n def preprocess_entailment(self, model, tokenizer):\n text_all = list(self.entailment_results.keys())\n text_batch, results_batch = [], []\n for i in range(0, len(text_all), self.entailment_batch_size):\n text_batch = text_all[i : min(len(text_all), i + self.entailment_batch_size)]\n batch_results = entailment_batch(text_batch, model, tokenizer)\n for sc in batch_results:\n results_batch.append(sc)\n for text, result in zip(text_batch, results_batch):\n self.entailment_results[text] = 1 if result else 0\n\n\nclass BaseExample:\n inf = \"-99999999\"\n \n def __init__(self, content):\n self.content = content.strip()\n self.steps = self.get_steps()\n self.step_labels = {}\n self.sequence_labels = []\n self.is_correct= False\n\n # Only for GSM8K dataset use\n def init_equations(self):\n raise NotImplementedError\n\n def get_steps(self):\n return [x+\"%%\" if x != self.content.split(\"%%\")[-1] else x for i, x in enumerate(self.content.split(\"%%\"))]\n\n def get_final_answer(self):\n ans = \"\"\n if \"####\" in self.content:\n ans = self.content.split(\"####\")[-1].strip().replace(\"%%\", \"\").replace(\" \", \"\")\n else:\n ans = BaseExample.inf\n return clean_ans(ans)\n\n def label_to_string(self):\n return \"\".join(str(self.labels[k]) for k in self.labels.keys())\n\n\nclass GSM8KExample(BaseExample):\n def __init__(self, content):\n super().__init__(content)\n self.equations = self.init_equations()\n self.verifier_score = 0.0\n\n # 按'<>'的格式将公式提取出来\n def init_equations(self):\n return [x for x in re.findall(\"<<.+>>[0-9\\.]+\", self.content) if \"=\" in x]\n\n def get_step_answer(step):\n expression = re.findall(\"<<.+>>[0-9\\.]+\", step)\n if len(expression == 0):\n ans = BaseExample.inf\n else:\n ans = expression[-1].split(\">>\")[-1].strip()\n return clean_ans(ans)\n ","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.GSM8KExample","uri":"program://CodeT/class/DIVERSE.code.src.utils.GSM8KExample#L160-L233","kind":"class","name":"GSM8KExample","path":"DIVERSE/code/src/utils.py","language":"python","start_line":160,"end_line":233,"context_start_line":140,"context_end_line":253,"code":"\n # Only for GSM8K dataset use\n def init_equations(self):\n raise NotImplementedError\n\n def get_steps(self):\n return [x+\"%%\" if x != self.content.split(\"%%\")[-1] else x for i, x in enumerate(self.content.split(\"%%\"))]\n\n def get_final_answer(self):\n ans = \"\"\n if \"####\" in self.content:\n ans = self.content.split(\"####\")[-1].strip().replace(\"%%\", \"\").replace(\" \", \"\")\n else:\n ans = BaseExample.inf\n return clean_ans(ans)\n\n def label_to_string(self):\n return \"\".join(str(self.labels[k]) for k in self.labels.keys())\n\n\nclass GSM8KExample(BaseExample):\n def __init__(self, content):\n super().__init__(content)\n self.equations = self.init_equations()\n self.verifier_score = 0.0\n\n # 按'<>'的格式将公式提取出来\n def init_equations(self):\n return [x for x in re.findall(\"<<.+>>[0-9\\.]+\", self.content) if \"=\" in x]\n\n def get_step_answer(step):\n expression = re.findall(\"<<.+>>[0-9\\.]+\", step)\n if len(expression == 0):\n ans = BaseExample.inf\n else:\n ans = expression[-1].split(\">>\")[-1].strip()\n return clean_ans(ans)\n \n @staticmethod\n @lru_cache(maxsize=4096)\n def get_answer(s):\n ans = \"\"\n if \"####\" in s:\n ans = s.split(\"####\")[-1].replace(\"%%\", \"\").replace(\" \", \"\").strip()\n else:\n expression = re.findall(\"<<.+>>[0-9\\.]+\", s)\n if len(expression) == 0:\n ans = GSM8KExample.inf\n else:\n ans = expression[-1].split(\">>\")[-1].strip()\n return clean_ans(ans)\n \n @staticmethod\n def match(steps, positive_examples, model=None, tokenizer=None):\n curr_set = Multiset([GSM8KExample.get_answer(x) for x in steps])\n for positive_example in positive_examples:\n golden_set = Multiset([GSM8KExample.get_answer(x) for x in positive_example.steps])\n if GSM8KExample.inf in curr_set:\n curr_set.remove(GSM8KExample.inf)\n if GSM8KExample.inf in golden_set:\n golden_set.remove(GSM8KExample.inf)\n if len(curr_set) == 0:\n return 0\n if curr_set.issubset(golden_set):\n return 1\n return 0\n \n def get_sequence_labels(question, pred):\n sequence_labels = []\n if pred.is_correct:\n sequence_labels.append((\"[CLS]\", \"SOLUTION-CORRECT\"))\n else:\n sequence_labels.append((\"[CLS]\", \"SOLUTION-INCORRECT\"))\n\n # add step tokens\n for s in pred.steps:\n token_list = [x for x in re.split(\"(>>| )\", s) if x != ' ']\n for token in token_list:\n if token == \">>\":\n if pred.step_labels[s] == 1:\n sequence_labels.append((token, \"STEP-CORRECT\"))\n else:\n sequence_labels.append((token, \"STEP-INCORRECT\"))\n else:\n sequence_labels.append((token, \"O\"))\n\n # add a split symbol\n sequence_labels.append((\"&&\", \"O\"))\n\n # add question tokens\n for token in question.split(\" \"):\n sequence_labels.append((token, \"O\"))\n\n return sequence_labels\n \n\nclass TextEntailmentExample(BaseExample):\n def __init__(self, content):\n super().__init__(content)\n\n @staticmethod\n def match(steps, positive_examples, model, tokenizer, entailment_result_dict):\n for pp in positive_examples:\n if TextEntailmentExample.match_per_example(pp, steps, entailment_result_dict):\n return 1\n return 0\n \n @staticmethod\n def match_per_example(pp, steps, entailment_result_dict):\n for k, step in enumerate(steps):\n if k >= len(pp.steps):\n continue\n # print(\"step:\", step)\n # print(\"pp.steps[k]:\", pp.steps[k])","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.TextEntailmentExample","uri":"program://CodeT/class/DIVERSE.code.src.utils.TextEntailmentExample#L236-L294","kind":"class","name":"TextEntailmentExample","path":"DIVERSE/code/src/utils.py","language":"python","start_line":236,"end_line":294,"context_start_line":216,"context_end_line":314,"code":" token_list = [x for x in re.split(\"(>>| )\", s) if x != ' ']\n for token in token_list:\n if token == \">>\":\n if pred.step_labels[s] == 1:\n sequence_labels.append((token, \"STEP-CORRECT\"))\n else:\n sequence_labels.append((token, \"STEP-INCORRECT\"))\n else:\n sequence_labels.append((token, \"O\"))\n\n # add a split symbol\n sequence_labels.append((\"&&\", \"O\"))\n\n # add question tokens\n for token in question.split(\" \"):\n sequence_labels.append((token, \"O\"))\n\n return sequence_labels\n \n\nclass TextEntailmentExample(BaseExample):\n def __init__(self, content):\n super().__init__(content)\n\n @staticmethod\n def match(steps, positive_examples, model, tokenizer, entailment_result_dict):\n for pp in positive_examples:\n if TextEntailmentExample.match_per_example(pp, steps, entailment_result_dict):\n return 1\n return 0\n \n @staticmethod\n def match_per_example(pp, steps, entailment_result_dict):\n for k, step in enumerate(steps):\n if k >= len(pp.steps):\n continue\n # print(\"step:\", step)\n # print(\"pp.steps[k]:\", pp.steps[k])\n pp_step = pp.steps[k].strip()\n text1 = f\"premise: {step} hypothesis: {pp_step}\"\n text2 = f\"premise: {pp_step} hypothesis: {step}\"\n if entailment_result_dict[text1] == 0 or entailment_result_dict[text2] == 0:\n # error_case = 'No, Christmas trees are not dissimilar to deciduous trees.%%Both Christmas trees and deciduous trees are types of trees.%%Both Christmas trees and deciduous trees have leaves.%%So the answer is no.#### no'\n # if error_case in text1 or error_case in text2:\n # print(\"text1:\", text1)\n # print(\"text2:\", text2)\n # pdb.set_trace()\n return 0\n return 1\n\n def get_sequence_labels(question, pred):\n sequence_labels = []\n if pred.is_correct:\n sequence_labels.append((\"[CLS]\", \"SOLUTION-CORRECT\"))\n else:\n sequence_labels.append((\"[CLS]\", \"SOLUTION-INCORRECT\"))\n\n # add step tokens\n for s in pred.steps:\n token_list = [x for x in re.split(\"(%%| )\", s) if x != ' ']\n for token in token_list:\n if token == \"\":\n continue\n if token == \"%%\":\n if pred.step_labels[s] == 1:\n sequence_labels.append((token, \"STEP-CORRECT\"))\n else:\n sequence_labels.append((token, \"STEP-INCORRECT\"))\n else:\n sequence_labels.append((token, \"O\"))\n\n # add a split symbol\n sequence_labels.append((\"&&\", \"O\"))\n\n # add question tokens\n for token in question.split(\" \"):\n sequence_labels.append((token, \"O\"))\n\n return sequence_labels\n\n\n@torch.no_grad()\ndef entailment_batch(text, model, tokenizer):\n inputs = tokenizer(text, padding=True, truncation=True, return_tensors=\"pt\").to(\"cuda\")\n labels = torch.tensor([1] * len(text)).to(\"cuda\")\n outputs = model(**inputs, labels=labels)\n logits = outputs.logits\n ans_list = torch.argmax(F.softmax(logits, dim=-1), dim=-1).tolist()\n ans_list = [x == model.config.label2id[\"ENTAILMENT\"] for x in ans_list]\n return ans_list\n\n\n@torch.no_grad()\ndef entailment(premise, hypothesis, model, tokenizer):\n text = f\"premise: {premise} hypothesis: {hypothesis}\"\n inputs = tokenizer(text, padding=True, truncation=True, return_tensors=\"pt\").to(model.device)\n labels = torch.tensor([1]).to(model.device)\n outputs = model(**inputs, labels=labels)\n logits = outputs.logits","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.entailment_batch","uri":"program://CodeT/function/DIVERSE.code.src.utils.entailment_batch#L298-L305","kind":"function","name":"entailment_batch","path":"DIVERSE/code/src/utils.py","language":"python","start_line":298,"end_line":305,"context_start_line":278,"context_end_line":325,"code":" continue\n if token == \"%%\":\n if pred.step_labels[s] == 1:\n sequence_labels.append((token, \"STEP-CORRECT\"))\n else:\n sequence_labels.append((token, \"STEP-INCORRECT\"))\n else:\n sequence_labels.append((token, \"O\"))\n\n # add a split symbol\n sequence_labels.append((\"&&\", \"O\"))\n\n # add question tokens\n for token in question.split(\" \"):\n sequence_labels.append((token, \"O\"))\n\n return sequence_labels\n\n\n@torch.no_grad()\ndef entailment_batch(text, model, tokenizer):\n inputs = tokenizer(text, padding=True, truncation=True, return_tensors=\"pt\").to(\"cuda\")\n labels = torch.tensor([1] * len(text)).to(\"cuda\")\n outputs = model(**inputs, labels=labels)\n logits = outputs.logits\n ans_list = torch.argmax(F.softmax(logits, dim=-1), dim=-1).tolist()\n ans_list = [x == model.config.label2id[\"ENTAILMENT\"] for x in ans_list]\n return ans_list\n\n\n@torch.no_grad()\ndef entailment(premise, hypothesis, model, tokenizer):\n text = f\"premise: {premise} hypothesis: {hypothesis}\"\n inputs = tokenizer(text, padding=True, truncation=True, return_tensors=\"pt\").to(model.device)\n labels = torch.tensor([1]).to(model.device)\n outputs = model(**inputs, labels=labels)\n logits = outputs.logits\n ans = torch.argmax(F.softmax(logits, dim=-1)).item() == model.config.label2id[\"ENTAILMENT\"]\n return ans\n\n\ndef convert_eval_sequences_to_cases(eval_sequences, pred_num_per_case, case_class, example_class):\n cases = []\n for i in range(0, len(eval_sequences), pred_num_per_case + 1):\n case = case_class(\"\", [])\n # question, grount_truth = eval_sequences[i].split(\"&&\")[0], eval_sequences[i].split(\"&&\")[1]\n question, grount_truth = eval_sequences[i].split(\"&&\")[1], eval_sequences[i].split(\"&&\")[0]\n case.ground_truth = example_class(grount_truth)","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.entailment","uri":"program://CodeT/function/DIVERSE.code.src.utils.entailment#L309-L316","kind":"function","name":"entailment","path":"DIVERSE/code/src/utils.py","language":"python","start_line":309,"end_line":316,"context_start_line":289,"context_end_line":336,"code":"\n # add question tokens\n for token in question.split(\" \"):\n sequence_labels.append((token, \"O\"))\n\n return sequence_labels\n\n\n@torch.no_grad()\ndef entailment_batch(text, model, tokenizer):\n inputs = tokenizer(text, padding=True, truncation=True, return_tensors=\"pt\").to(\"cuda\")\n labels = torch.tensor([1] * len(text)).to(\"cuda\")\n outputs = model(**inputs, labels=labels)\n logits = outputs.logits\n ans_list = torch.argmax(F.softmax(logits, dim=-1), dim=-1).tolist()\n ans_list = [x == model.config.label2id[\"ENTAILMENT\"] for x in ans_list]\n return ans_list\n\n\n@torch.no_grad()\ndef entailment(premise, hypothesis, model, tokenizer):\n text = f\"premise: {premise} hypothesis: {hypothesis}\"\n inputs = tokenizer(text, padding=True, truncation=True, return_tensors=\"pt\").to(model.device)\n labels = torch.tensor([1]).to(model.device)\n outputs = model(**inputs, labels=labels)\n logits = outputs.logits\n ans = torch.argmax(F.softmax(logits, dim=-1)).item() == model.config.label2id[\"ENTAILMENT\"]\n return ans\n\n\ndef convert_eval_sequences_to_cases(eval_sequences, pred_num_per_case, case_class, example_class):\n cases = []\n for i in range(0, len(eval_sequences), pred_num_per_case + 1):\n case = case_class(\"\", [])\n # question, grount_truth = eval_sequences[i].split(\"&&\")[0], eval_sequences[i].split(\"&&\")[1]\n question, grount_truth = eval_sequences[i].split(\"&&\")[1], eval_sequences[i].split(\"&&\")[0]\n case.ground_truth = example_class(grount_truth)\n case.question = question\n for j in range(i+1, i+pred_num_per_case+1):\n # case.preds.append(GSM8KExample(eval_sequences[j].split(\"&&\")[1]))\n case.preds.append(example_class(eval_sequences[j].split(\"&&\")[0]))\n cases.append(case)\n # if example_class.__name__ == \"TextEntailmentExample\":\n # cases = post_process_answer_clutrr(cases)\n return cases\n\n\ndef post_process_answer_clutrr_mapping(cases):","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.convert_eval_sequences_to_cases","uri":"program://CodeT/function/DIVERSE.code.src.utils.convert_eval_sequences_to_cases#L319-L333","kind":"function","name":"convert_eval_sequences_to_cases","path":"DIVERSE/code/src/utils.py","language":"python","start_line":319,"end_line":333,"context_start_line":299,"context_end_line":353,"code":" inputs = tokenizer(text, padding=True, truncation=True, return_tensors=\"pt\").to(\"cuda\")\n labels = torch.tensor([1] * len(text)).to(\"cuda\")\n outputs = model(**inputs, labels=labels)\n logits = outputs.logits\n ans_list = torch.argmax(F.softmax(logits, dim=-1), dim=-1).tolist()\n ans_list = [x == model.config.label2id[\"ENTAILMENT\"] for x in ans_list]\n return ans_list\n\n\n@torch.no_grad()\ndef entailment(premise, hypothesis, model, tokenizer):\n text = f\"premise: {premise} hypothesis: {hypothesis}\"\n inputs = tokenizer(text, padding=True, truncation=True, return_tensors=\"pt\").to(model.device)\n labels = torch.tensor([1]).to(model.device)\n outputs = model(**inputs, labels=labels)\n logits = outputs.logits\n ans = torch.argmax(F.softmax(logits, dim=-1)).item() == model.config.label2id[\"ENTAILMENT\"]\n return ans\n\n\ndef convert_eval_sequences_to_cases(eval_sequences, pred_num_per_case, case_class, example_class):\n cases = []\n for i in range(0, len(eval_sequences), pred_num_per_case + 1):\n case = case_class(\"\", [])\n # question, grount_truth = eval_sequences[i].split(\"&&\")[0], eval_sequences[i].split(\"&&\")[1]\n question, grount_truth = eval_sequences[i].split(\"&&\")[1], eval_sequences[i].split(\"&&\")[0]\n case.ground_truth = example_class(grount_truth)\n case.question = question\n for j in range(i+1, i+pred_num_per_case+1):\n # case.preds.append(GSM8KExample(eval_sequences[j].split(\"&&\")[1]))\n case.preds.append(example_class(eval_sequences[j].split(\"&&\")[0]))\n cases.append(case)\n # if example_class.__name__ == \"TextEntailmentExample\":\n # cases = post_process_answer_clutrr(cases)\n return cases\n\n\ndef post_process_answer_clutrr_mapping(cases):\n print(\"before loading pipeline\")\n classifier = pipeline(\"zero-shot-classification\", device=0)\n print(\"after loading pipeline\")\n print(\"post processing\")\n candidate_labels = ['sister', 'son', 'aunt', 'granddaughter', 'father', 'grandfather', 'grandmother', 'mother-in-law', 'uncle', 'niece', 'mother', 'brother', 'daughter', 'nephew', 'grandson', 'son-in-law', 'father-in-law', 'daughter-in-law']\n for case_idx, case in tqdm(enumerate(cases)):\n gt_ans = case.ground_truth.get_final_answer()\n # skip StrategyQA task\n if gt_ans == \"yes\" or gt_ans == \"no\":\n break\n for pred in case.preds:\n pred_ans = pred.get_final_answer()\n if pred_ans != BaseExample.inf and pred_ans != gt_ans:\n outputs = classifier(pred_ans, candidate_labels)\n logits = outputs[\"scores\"]\n labels = outputs[\"labels\"]\n candidate_index = np.argmax(logits)","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.post_process_answer_clutrr_mapping","uri":"program://CodeT/function/DIVERSE.code.src.utils.post_process_answer_clutrr_mapping#L336-L358","kind":"function","name":"post_process_answer_clutrr_mapping","path":"DIVERSE/code/src/utils.py","language":"python","start_line":336,"end_line":358,"context_start_line":316,"context_end_line":378,"code":" return ans\n\n\ndef convert_eval_sequences_to_cases(eval_sequences, pred_num_per_case, case_class, example_class):\n cases = []\n for i in range(0, len(eval_sequences), pred_num_per_case + 1):\n case = case_class(\"\", [])\n # question, grount_truth = eval_sequences[i].split(\"&&\")[0], eval_sequences[i].split(\"&&\")[1]\n question, grount_truth = eval_sequences[i].split(\"&&\")[1], eval_sequences[i].split(\"&&\")[0]\n case.ground_truth = example_class(grount_truth)\n case.question = question\n for j in range(i+1, i+pred_num_per_case+1):\n # case.preds.append(GSM8KExample(eval_sequences[j].split(\"&&\")[1]))\n case.preds.append(example_class(eval_sequences[j].split(\"&&\")[0]))\n cases.append(case)\n # if example_class.__name__ == \"TextEntailmentExample\":\n # cases = post_process_answer_clutrr(cases)\n return cases\n\n\ndef post_process_answer_clutrr_mapping(cases):\n print(\"before loading pipeline\")\n classifier = pipeline(\"zero-shot-classification\", device=0)\n print(\"after loading pipeline\")\n print(\"post processing\")\n candidate_labels = ['sister', 'son', 'aunt', 'granddaughter', 'father', 'grandfather', 'grandmother', 'mother-in-law', 'uncle', 'niece', 'mother', 'brother', 'daughter', 'nephew', 'grandson', 'son-in-law', 'father-in-law', 'daughter-in-law']\n for case_idx, case in tqdm(enumerate(cases)):\n gt_ans = case.ground_truth.get_final_answer()\n # skip StrategyQA task\n if gt_ans == \"yes\" or gt_ans == \"no\":\n break\n for pred in case.preds:\n pred_ans = pred.get_final_answer()\n if pred_ans != BaseExample.inf and pred_ans != gt_ans:\n outputs = classifier(pred_ans, candidate_labels)\n logits = outputs[\"scores\"]\n labels = outputs[\"labels\"]\n candidate_index = np.argmax(logits)\n most_similar_answer = labels[candidate_index]\n body = pred.content.split(\"####\")[0]\n pred.content = body + \"####\" + most_similar_answer\n # pdb.set_trace()\n return cases\n \n\ndef post_process_answer_clutrr_cutoff(cases):\n candidate_labels = ['sister', 'son', 'aunt', 'granddaughter', 'father', 'grandfather', 'grandmother', 'mother-in-law', 'uncle', 'niece', 'mother', 'brother', 'daughter', 'nephew', 'grandson', 'son-in-law', 'father-in-law', 'daughter-in-law']\n for case_idx, case in tqdm(enumerate(cases)):\n gt_ans = case.ground_truth.get_final_answer()\n # skip StrategyQA task\n if gt_ans == \"yes\" or gt_ans == \"no\":\n break\n for pred in case.preds:\n pred_ans = pred.get_final_answer()\n if pred_ans not in candidate_labels:\n body = pred.content.split(\"####\")[0]\n pred.content = body + \"####\" + BaseExample.inf\n return cases\n\n\ndef random_1_hit(gt_ans, preds):\n idx = random.randint(0, len(preds)-1)\n # random 1 acc","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.post_process_answer_clutrr_cutoff","uri":"program://CodeT/function/DIVERSE.code.src.utils.post_process_answer_clutrr_cutoff#L361-L373","kind":"function","name":"post_process_answer_clutrr_cutoff","path":"DIVERSE/code/src/utils.py","language":"python","start_line":361,"end_line":373,"context_start_line":341,"context_end_line":393,"code":" candidate_labels = ['sister', 'son', 'aunt', 'granddaughter', 'father', 'grandfather', 'grandmother', 'mother-in-law', 'uncle', 'niece', 'mother', 'brother', 'daughter', 'nephew', 'grandson', 'son-in-law', 'father-in-law', 'daughter-in-law']\n for case_idx, case in tqdm(enumerate(cases)):\n gt_ans = case.ground_truth.get_final_answer()\n # skip StrategyQA task\n if gt_ans == \"yes\" or gt_ans == \"no\":\n break\n for pred in case.preds:\n pred_ans = pred.get_final_answer()\n if pred_ans != BaseExample.inf and pred_ans != gt_ans:\n outputs = classifier(pred_ans, candidate_labels)\n logits = outputs[\"scores\"]\n labels = outputs[\"labels\"]\n candidate_index = np.argmax(logits)\n most_similar_answer = labels[candidate_index]\n body = pred.content.split(\"####\")[0]\n pred.content = body + \"####\" + most_similar_answer\n # pdb.set_trace()\n return cases\n \n\ndef post_process_answer_clutrr_cutoff(cases):\n candidate_labels = ['sister', 'son', 'aunt', 'granddaughter', 'father', 'grandfather', 'grandmother', 'mother-in-law', 'uncle', 'niece', 'mother', 'brother', 'daughter', 'nephew', 'grandson', 'son-in-law', 'father-in-law', 'daughter-in-law']\n for case_idx, case in tqdm(enumerate(cases)):\n gt_ans = case.ground_truth.get_final_answer()\n # skip StrategyQA task\n if gt_ans == \"yes\" or gt_ans == \"no\":\n break\n for pred in case.preds:\n pred_ans = pred.get_final_answer()\n if pred_ans not in candidate_labels:\n body = pred.content.split(\"####\")[0]\n pred.content = body + \"####\" + BaseExample.inf\n return cases\n\n\ndef random_1_hit(gt_ans, preds):\n idx = random.randint(0, len(preds)-1)\n # random 1 acc\n pred0_ans = preds[idx].get_final_answer()\n return 1 if pred0_ans == gt_ans else 0\n\n\ndef recall_hit(gt_ans, preds):\n for pred in preds:\n if pred.get_final_answer() == gt_ans:\n return 1\n return 0\n\n\ndef voting_hit(gt_ans, preds):\n # voting acc\n answers = {}\n for pred in preds:","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.random_1_hit","uri":"program://CodeT/function/DIVERSE.code.src.utils.random_1_hit#L376-L380","kind":"function","name":"random_1_hit","path":"DIVERSE/code/src/utils.py","language":"python","start_line":376,"end_line":380,"context_start_line":356,"context_end_line":400,"code":" pred.content = body + \"####\" + most_similar_answer\n # pdb.set_trace()\n return cases\n \n\ndef post_process_answer_clutrr_cutoff(cases):\n candidate_labels = ['sister', 'son', 'aunt', 'granddaughter', 'father', 'grandfather', 'grandmother', 'mother-in-law', 'uncle', 'niece', 'mother', 'brother', 'daughter', 'nephew', 'grandson', 'son-in-law', 'father-in-law', 'daughter-in-law']\n for case_idx, case in tqdm(enumerate(cases)):\n gt_ans = case.ground_truth.get_final_answer()\n # skip StrategyQA task\n if gt_ans == \"yes\" or gt_ans == \"no\":\n break\n for pred in case.preds:\n pred_ans = pred.get_final_answer()\n if pred_ans not in candidate_labels:\n body = pred.content.split(\"####\")[0]\n pred.content = body + \"####\" + BaseExample.inf\n return cases\n\n\ndef random_1_hit(gt_ans, preds):\n idx = random.randint(0, len(preds)-1)\n # random 1 acc\n pred0_ans = preds[idx].get_final_answer()\n return 1 if pred0_ans == gt_ans else 0\n\n\ndef recall_hit(gt_ans, preds):\n for pred in preds:\n if pred.get_final_answer() == gt_ans:\n return 1\n return 0\n\n\ndef voting_hit(gt_ans, preds):\n # voting acc\n answers = {}\n for pred in preds:\n if pred.get_final_answer() not in answers:\n answers[pred.get_final_answer()] = 0\n answers[pred.get_final_answer()] += 1\n answers = sorted(answers.items(), key=lambda x : x[1], reverse=True)\n for i in range(len(answers)):\n ans, ans_cnt = answers[i][0], answers[i][1]\n if ans != GSM8KExample.inf:","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.recall_hit","uri":"program://CodeT/function/DIVERSE.code.src.utils.recall_hit#L383-L387","kind":"function","name":"recall_hit","path":"DIVERSE/code/src/utils.py","language":"python","start_line":383,"end_line":387,"context_start_line":363,"context_end_line":407,"code":" for case_idx, case in tqdm(enumerate(cases)):\n gt_ans = case.ground_truth.get_final_answer()\n # skip StrategyQA task\n if gt_ans == \"yes\" or gt_ans == \"no\":\n break\n for pred in case.preds:\n pred_ans = pred.get_final_answer()\n if pred_ans not in candidate_labels:\n body = pred.content.split(\"####\")[0]\n pred.content = body + \"####\" + BaseExample.inf\n return cases\n\n\ndef random_1_hit(gt_ans, preds):\n idx = random.randint(0, len(preds)-1)\n # random 1 acc\n pred0_ans = preds[idx].get_final_answer()\n return 1 if pred0_ans == gt_ans else 0\n\n\ndef recall_hit(gt_ans, preds):\n for pred in preds:\n if pred.get_final_answer() == gt_ans:\n return 1\n return 0\n\n\ndef voting_hit(gt_ans, preds):\n # voting acc\n answers = {}\n for pred in preds:\n if pred.get_final_answer() not in answers:\n answers[pred.get_final_answer()] = 0\n answers[pred.get_final_answer()] += 1\n answers = sorted(answers.items(), key=lambda x : x[1], reverse=True)\n for i in range(len(answers)):\n ans, ans_cnt = answers[i][0], answers[i][1]\n if ans != GSM8KExample.inf:\n return 1 if ans == gt_ans else 0\n return 0\n\n\ndef weighted_voting_hit(gt_ans, preds):\n # voting acc\n answers = {}","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.voting_hit","uri":"program://CodeT/function/DIVERSE.code.src.utils.voting_hit#L390-L402","kind":"function","name":"voting_hit","path":"DIVERSE/code/src/utils.py","language":"python","start_line":390,"end_line":402,"context_start_line":370,"context_end_line":422,"code":" if pred_ans not in candidate_labels:\n body = pred.content.split(\"####\")[0]\n pred.content = body + \"####\" + BaseExample.inf\n return cases\n\n\ndef random_1_hit(gt_ans, preds):\n idx = random.randint(0, len(preds)-1)\n # random 1 acc\n pred0_ans = preds[idx].get_final_answer()\n return 1 if pred0_ans == gt_ans else 0\n\n\ndef recall_hit(gt_ans, preds):\n for pred in preds:\n if pred.get_final_answer() == gt_ans:\n return 1\n return 0\n\n\ndef voting_hit(gt_ans, preds):\n # voting acc\n answers = {}\n for pred in preds:\n if pred.get_final_answer() not in answers:\n answers[pred.get_final_answer()] = 0\n answers[pred.get_final_answer()] += 1\n answers = sorted(answers.items(), key=lambda x : x[1], reverse=True)\n for i in range(len(answers)):\n ans, ans_cnt = answers[i][0], answers[i][1]\n if ans != GSM8KExample.inf:\n return 1 if ans == gt_ans else 0\n return 0\n\n\ndef weighted_voting_hit(gt_ans, preds):\n # voting acc\n answers = {}\n for pred in preds:\n if pred.get_final_answer() not in answers:\n answers[pred.get_final_answer()] = 0\n answers[pred.get_final_answer()] += pred.verifier_score\n answers = sorted(answers.items(), key=lambda x : x[1], reverse=True)\n for i in range(len(answers)):\n ans, ans_cnt = answers[i][0], answers[i][1]\n if ans != GSM8KExample.inf:\n return 1 if ans == gt_ans else 0\n return 0\n\n\ndef verification_hit(gt_ans, preds):\n preds = sorted(preds, key=lambda x : x.verifier_score, reverse=True)\n for pred in preds:","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.weighted_voting_hit","uri":"program://CodeT/function/DIVERSE.code.src.utils.weighted_voting_hit#L405-L417","kind":"function","name":"weighted_voting_hit","path":"DIVERSE/code/src/utils.py","language":"python","start_line":405,"end_line":417,"context_start_line":385,"context_end_line":437,"code":" if pred.get_final_answer() == gt_ans:\n return 1\n return 0\n\n\ndef voting_hit(gt_ans, preds):\n # voting acc\n answers = {}\n for pred in preds:\n if pred.get_final_answer() not in answers:\n answers[pred.get_final_answer()] = 0\n answers[pred.get_final_answer()] += 1\n answers = sorted(answers.items(), key=lambda x : x[1], reverse=True)\n for i in range(len(answers)):\n ans, ans_cnt = answers[i][0], answers[i][1]\n if ans != GSM8KExample.inf:\n return 1 if ans == gt_ans else 0\n return 0\n\n\ndef weighted_voting_hit(gt_ans, preds):\n # voting acc\n answers = {}\n for pred in preds:\n if pred.get_final_answer() not in answers:\n answers[pred.get_final_answer()] = 0\n answers[pred.get_final_answer()] += pred.verifier_score\n answers = sorted(answers.items(), key=lambda x : x[1], reverse=True)\n for i in range(len(answers)):\n ans, ans_cnt = answers[i][0], answers[i][1]\n if ans != GSM8KExample.inf:\n return 1 if ans == gt_ans else 0\n return 0\n\n\ndef verification_hit(gt_ans, preds):\n preds = sorted(preds, key=lambda x : x.verifier_score, reverse=True)\n for pred in preds:\n ans = pred.get_final_answer()\n if ans != GSM8KExample.inf:\n return 1 if ans == gt_ans else 0\n return 0\n\n\ndef compute_top1_and_recall(data, rand_k=100):\n total_random_hit_cnt = 0\n total_vote_cnt = 0\n total_recall_cnt = 0\n for i, x in enumerate(data):\n gt_ans = x.ground_truth.get_final_answer()\n slice = x.preds if rand_k >= len(x.preds) else random.sample(x.preds, rand_k)\n \n total_random_hit_cnt += random_1_hit(gt_ans, slice)","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.verification_hit","uri":"program://CodeT/function/DIVERSE.code.src.utils.verification_hit#L420-L426","kind":"function","name":"verification_hit","path":"DIVERSE/code/src/utils.py","language":"python","start_line":420,"end_line":426,"context_start_line":400,"context_end_line":446,"code":" if ans != GSM8KExample.inf:\n return 1 if ans == gt_ans else 0\n return 0\n\n\ndef weighted_voting_hit(gt_ans, preds):\n # voting acc\n answers = {}\n for pred in preds:\n if pred.get_final_answer() not in answers:\n answers[pred.get_final_answer()] = 0\n answers[pred.get_final_answer()] += pred.verifier_score\n answers = sorted(answers.items(), key=lambda x : x[1], reverse=True)\n for i in range(len(answers)):\n ans, ans_cnt = answers[i][0], answers[i][1]\n if ans != GSM8KExample.inf:\n return 1 if ans == gt_ans else 0\n return 0\n\n\ndef verification_hit(gt_ans, preds):\n preds = sorted(preds, key=lambda x : x.verifier_score, reverse=True)\n for pred in preds:\n ans = pred.get_final_answer()\n if ans != GSM8KExample.inf:\n return 1 if ans == gt_ans else 0\n return 0\n\n\ndef compute_top1_and_recall(data, rand_k=100):\n total_random_hit_cnt = 0\n total_vote_cnt = 0\n total_recall_cnt = 0\n for i, x in enumerate(data):\n gt_ans = x.ground_truth.get_final_answer()\n slice = x.preds if rand_k >= len(x.preds) else random.sample(x.preds, rand_k)\n \n total_random_hit_cnt += random_1_hit(gt_ans, slice)\n total_vote_cnt += voting_hit(gt_ans, slice)\n total_recall_cnt += recall_hit(gt_ans, slice)\n result = {\n \"random_top1\": total_random_hit_cnt / len(data), \n \"voting_top1_accuracy\": total_vote_cnt / len(data),\n \"recall\": total_recall_cnt / len(data),\n }\n return result\n","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.compute_top1_and_recall","uri":"program://CodeT/function/DIVERSE.code.src.utils.compute_top1_and_recall#L429-L445","kind":"function","name":"compute_top1_and_recall","path":"DIVERSE/code/src/utils.py","language":"python","start_line":429,"end_line":445,"context_start_line":409,"context_end_line":465,"code":" if pred.get_final_answer() not in answers:\n answers[pred.get_final_answer()] = 0\n answers[pred.get_final_answer()] += pred.verifier_score\n answers = sorted(answers.items(), key=lambda x : x[1], reverse=True)\n for i in range(len(answers)):\n ans, ans_cnt = answers[i][0], answers[i][1]\n if ans != GSM8KExample.inf:\n return 1 if ans == gt_ans else 0\n return 0\n\n\ndef verification_hit(gt_ans, preds):\n preds = sorted(preds, key=lambda x : x.verifier_score, reverse=True)\n for pred in preds:\n ans = pred.get_final_answer()\n if ans != GSM8KExample.inf:\n return 1 if ans == gt_ans else 0\n return 0\n\n\ndef compute_top1_and_recall(data, rand_k=100):\n total_random_hit_cnt = 0\n total_vote_cnt = 0\n total_recall_cnt = 0\n for i, x in enumerate(data):\n gt_ans = x.ground_truth.get_final_answer()\n slice = x.preds if rand_k >= len(x.preds) else random.sample(x.preds, rand_k)\n \n total_random_hit_cnt += random_1_hit(gt_ans, slice)\n total_vote_cnt += voting_hit(gt_ans, slice)\n total_recall_cnt += recall_hit(gt_ans, slice)\n result = {\n \"random_top1\": total_random_hit_cnt / len(data), \n \"voting_top1_accuracy\": total_vote_cnt / len(data),\n \"recall\": total_recall_cnt / len(data),\n }\n return result\n\n\ndef compute_results(data, rand_k=100):\n total_random_hit_cnt = 0\n total_recall_cnt = 0\n total_vote_cnt = 0\n total_weighted_vote_cnt = 0\n total_verification_cnt = 0\n for i, x in enumerate(data):\n gt_ans = x.ground_truth.get_final_answer()\n slice = x.preds if rand_k == len(x.preds) else random.sample(x.preds, rand_k)\n \n total_random_hit_cnt += random_1_hit(gt_ans, slice)\n total_vote_cnt += voting_hit(gt_ans, slice)\n total_recall_cnt += recall_hit(gt_ans, slice)\n total_weighted_vote_cnt += weighted_voting_hit(gt_ans, slice)\n total_verification_cnt += verification_hit(gt_ans, slice)\n result = {\n \"random_top1\": total_random_hit_cnt / len(data), \n f\"recall@{rand_k}\": total_recall_cnt / len(data),","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.compute_results","uri":"program://CodeT/function/DIVERSE.code.src.utils.compute_results#L448-L470","kind":"function","name":"compute_results","path":"DIVERSE/code/src/utils.py","language":"python","start_line":448,"end_line":470,"context_start_line":428,"context_end_line":490,"code":"\ndef compute_top1_and_recall(data, rand_k=100):\n total_random_hit_cnt = 0\n total_vote_cnt = 0\n total_recall_cnt = 0\n for i, x in enumerate(data):\n gt_ans = x.ground_truth.get_final_answer()\n slice = x.preds if rand_k >= len(x.preds) else random.sample(x.preds, rand_k)\n \n total_random_hit_cnt += random_1_hit(gt_ans, slice)\n total_vote_cnt += voting_hit(gt_ans, slice)\n total_recall_cnt += recall_hit(gt_ans, slice)\n result = {\n \"random_top1\": total_random_hit_cnt / len(data), \n \"voting_top1_accuracy\": total_vote_cnt / len(data),\n \"recall\": total_recall_cnt / len(data),\n }\n return result\n\n\ndef compute_results(data, rand_k=100):\n total_random_hit_cnt = 0\n total_recall_cnt = 0\n total_vote_cnt = 0\n total_weighted_vote_cnt = 0\n total_verification_cnt = 0\n for i, x in enumerate(data):\n gt_ans = x.ground_truth.get_final_answer()\n slice = x.preds if rand_k == len(x.preds) else random.sample(x.preds, rand_k)\n \n total_random_hit_cnt += random_1_hit(gt_ans, slice)\n total_vote_cnt += voting_hit(gt_ans, slice)\n total_recall_cnt += recall_hit(gt_ans, slice)\n total_weighted_vote_cnt += weighted_voting_hit(gt_ans, slice)\n total_verification_cnt += verification_hit(gt_ans, slice)\n result = {\n \"random_top1\": total_random_hit_cnt / len(data), \n f\"recall@{rand_k}\": total_recall_cnt / len(data),\n f\"verifier_top1_accuracy@{rand_k}\": total_verification_cnt / len(data),\n f\"voting_top1_accuracy@{rand_k}\": total_vote_cnt / len(data),\n f\"weighted_voting_top1_accuracy@{rand_k}\": total_weighted_vote_cnt / len(data),\n }\n return result\n\n\ndef compute_results_avg(data, rand_k=100, repeat_time=5):\n sum_result_dict = {\n \"random_top1\": 0, \n f\"recall@{rand_k}\": 0,\n f\"verifier_top1_accuracy@{rand_k}\": 0,\n f\"voting_top1_accuracy@{rand_k}\": 0,\n f\"weighted_voting_top1_accuracy@{rand_k}\": 0,\n }\n for i in tqdm(range(repeat_time)):\n for k in sum_result_dict:\n result_dict = compute_results(data, rand_k=rand_k)\n sum_result_dict[k] += result_dict[k]\n for k in sum_result_dict:\n sum_result_dict[k] = sum_result_dict[k] / repeat_time if repeat_time != 1 else sum_result_dict[k]\n sum_result_dict[k] = round(sum_result_dict[k], 8)\n return sum_result_dict\n \n","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.compute_results_avg","uri":"program://CodeT/function/DIVERSE.code.src.utils.compute_results_avg#L473-L488","kind":"function","name":"compute_results_avg","path":"DIVERSE/code/src/utils.py","language":"python","start_line":473,"end_line":488,"context_start_line":453,"context_end_line":508,"code":" total_verification_cnt = 0\n for i, x in enumerate(data):\n gt_ans = x.ground_truth.get_final_answer()\n slice = x.preds if rand_k == len(x.preds) else random.sample(x.preds, rand_k)\n \n total_random_hit_cnt += random_1_hit(gt_ans, slice)\n total_vote_cnt += voting_hit(gt_ans, slice)\n total_recall_cnt += recall_hit(gt_ans, slice)\n total_weighted_vote_cnt += weighted_voting_hit(gt_ans, slice)\n total_verification_cnt += verification_hit(gt_ans, slice)\n result = {\n \"random_top1\": total_random_hit_cnt / len(data), \n f\"recall@{rand_k}\": total_recall_cnt / len(data),\n f\"verifier_top1_accuracy@{rand_k}\": total_verification_cnt / len(data),\n f\"voting_top1_accuracy@{rand_k}\": total_vote_cnt / len(data),\n f\"weighted_voting_top1_accuracy@{rand_k}\": total_weighted_vote_cnt / len(data),\n }\n return result\n\n\ndef compute_results_avg(data, rand_k=100, repeat_time=5):\n sum_result_dict = {\n \"random_top1\": 0, \n f\"recall@{rand_k}\": 0,\n f\"verifier_top1_accuracy@{rand_k}\": 0,\n f\"voting_top1_accuracy@{rand_k}\": 0,\n f\"weighted_voting_top1_accuracy@{rand_k}\": 0,\n }\n for i in tqdm(range(repeat_time)):\n for k in sum_result_dict:\n result_dict = compute_results(data, rand_k=rand_k)\n sum_result_dict[k] += result_dict[k]\n for k in sum_result_dict:\n sum_result_dict[k] = sum_result_dict[k] / repeat_time if repeat_time != 1 else sum_result_dict[k]\n sum_result_dict[k] = round(sum_result_dict[k], 8)\n return sum_result_dict\n \n\ndef dedup(li):\n s = set()\n new_li = []\n for x in li:\n if str(x) not in s:\n new_li.append(x)\n s.add(str(x))\n return new_li\n\n\ndef print_stat(data):\n cnt = 0\n for x in data:\n if x[\"output\"] == \"correct\":\n cnt += 1\n print(cnt, len(data) - cnt, len(data))\n\n","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.dedup","uri":"program://CodeT/function/DIVERSE.code.src.utils.dedup#L491-L498","kind":"function","name":"dedup","path":"DIVERSE/code/src/utils.py","language":"python","start_line":491,"end_line":498,"context_start_line":471,"context_end_line":513,"code":"\n\ndef compute_results_avg(data, rand_k=100, repeat_time=5):\n sum_result_dict = {\n \"random_top1\": 0, \n f\"recall@{rand_k}\": 0,\n f\"verifier_top1_accuracy@{rand_k}\": 0,\n f\"voting_top1_accuracy@{rand_k}\": 0,\n f\"weighted_voting_top1_accuracy@{rand_k}\": 0,\n }\n for i in tqdm(range(repeat_time)):\n for k in sum_result_dict:\n result_dict = compute_results(data, rand_k=rand_k)\n sum_result_dict[k] += result_dict[k]\n for k in sum_result_dict:\n sum_result_dict[k] = sum_result_dict[k] / repeat_time if repeat_time != 1 else sum_result_dict[k]\n sum_result_dict[k] = round(sum_result_dict[k], 8)\n return sum_result_dict\n \n\ndef dedup(li):\n s = set()\n new_li = []\n for x in li:\n if str(x) not in s:\n new_li.append(x)\n s.add(str(x))\n return new_li\n\n\ndef print_stat(data):\n cnt = 0\n for x in data:\n if x[\"output\"] == \"correct\":\n cnt += 1\n print(cnt, len(data) - cnt, len(data))\n\n\ndef clean_ans(s):\n s = str(s)\n if s and len(s) > 0 and s[-1] == '.':\n s = s[:-1]\n return s.lower() # for CLUTRR and strategyQA use","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.print_stat","uri":"program://CodeT/function/DIVERSE.code.src.utils.print_stat#L501-L506","kind":"function","name":"print_stat","path":"DIVERSE/code/src/utils.py","language":"python","start_line":501,"end_line":506,"context_start_line":481,"context_end_line":513,"code":" for i in tqdm(range(repeat_time)):\n for k in sum_result_dict:\n result_dict = compute_results(data, rand_k=rand_k)\n sum_result_dict[k] += result_dict[k]\n for k in sum_result_dict:\n sum_result_dict[k] = sum_result_dict[k] / repeat_time if repeat_time != 1 else sum_result_dict[k]\n sum_result_dict[k] = round(sum_result_dict[k], 8)\n return sum_result_dict\n \n\ndef dedup(li):\n s = set()\n new_li = []\n for x in li:\n if str(x) not in s:\n new_li.append(x)\n s.add(str(x))\n return new_li\n\n\ndef print_stat(data):\n cnt = 0\n for x in data:\n if x[\"output\"] == \"correct\":\n cnt += 1\n print(cnt, len(data) - cnt, len(data))\n\n\ndef clean_ans(s):\n s = str(s)\n if s and len(s) > 0 and s[-1] == '.':\n s = s[:-1]\n return s.lower() # for CLUTRR and strategyQA use","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.clean_ans","uri":"program://CodeT/function/DIVERSE.code.src.utils.clean_ans#L509-L513","kind":"function","name":"clean_ans","path":"DIVERSE/code/src/utils.py","language":"python","start_line":509,"end_line":513,"context_start_line":489,"context_end_line":513,"code":" \n\ndef dedup(li):\n s = set()\n new_li = []\n for x in li:\n if str(x) not in s:\n new_li.append(x)\n s.add(str(x))\n return new_li\n\n\ndef print_stat(data):\n cnt = 0\n for x in data:\n if x[\"output\"] == \"correct\":\n cnt += 1\n print(cnt, len(data) - cnt, len(data))\n\n\ndef clean_ans(s):\n s = str(s)\n if s and len(s) > 0 and s[-1] == '.':\n s = s[:-1]\n return s.lower() # for CLUTRR and strategyQA use","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.__init__","uri":"program://CodeT/function/DIVERSE.code.src.utils.__init__#L237-L238","kind":"function","name":"__init__","path":"DIVERSE/code/src/utils.py","language":"python","start_line":237,"end_line":238,"context_start_line":217,"context_end_line":258,"code":" for token in token_list:\n if token == \">>\":\n if pred.step_labels[s] == 1:\n sequence_labels.append((token, \"STEP-CORRECT\"))\n else:\n sequence_labels.append((token, \"STEP-INCORRECT\"))\n else:\n sequence_labels.append((token, \"O\"))\n\n # add a split symbol\n sequence_labels.append((\"&&\", \"O\"))\n\n # add question tokens\n for token in question.split(\" \"):\n sequence_labels.append((token, \"O\"))\n\n return sequence_labels\n \n\nclass TextEntailmentExample(BaseExample):\n def __init__(self, content):\n super().__init__(content)\n\n @staticmethod\n def match(steps, positive_examples, model, tokenizer, entailment_result_dict):\n for pp in positive_examples:\n if TextEntailmentExample.match_per_example(pp, steps, entailment_result_dict):\n return 1\n return 0\n \n @staticmethod\n def match_per_example(pp, steps, entailment_result_dict):\n for k, step in enumerate(steps):\n if k >= len(pp.steps):\n continue\n # print(\"step:\", step)\n # print(\"pp.steps[k]:\", pp.steps[k])\n pp_step = pp.steps[k].strip()\n text1 = f\"premise: {step} hypothesis: {pp_step}\"\n text2 = f\"premise: {pp_step} hypothesis: {step}\"\n if entailment_result_dict[text1] == 0 or entailment_result_dict[text2] == 0:\n # error_case = 'No, Christmas trees are not dissimilar to deciduous trees.%%Both Christmas trees and deciduous trees are types of trees.%%Both Christmas trees and deciduous trees have leaves.%%So the answer is no.#### no'","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.do_step_labeling","uri":"program://CodeT/function/DIVERSE.code.src.utils.do_step_labeling#L67-L102","kind":"function","name":"do_step_labeling","path":"DIVERSE/code/src/utils.py","language":"python","start_line":67,"end_line":102,"context_start_line":47,"context_end_line":122,"code":" pred.is_correct = True\n for step in pred.steps:\n pred.step_labels[step] = 1\n else:\n for k, step in enumerate(pred.steps):\n ans = GSM8KExample.match(\n pred.steps[:k+1],\n positive_preds,\n model=model,\n tokenizer=tokenizer,\n )\n pred.step_labels[step] = ans\n\n\nclass TextEntailmentCase(BaseCase):\n def __init__(self, ground_truth, preds, entailment_batch_size=512):\n super().__init__(ground_truth, preds)\n self.entailment_results = {}\n self.entailment_batch_size = entailment_batch_size\n\n def do_step_labeling(self, model=None, tokenizer=None):\n # 将ground_truth标记为true\n self.ground_truth.is_correct = True\n for step in self.ground_truth.steps:\n self.ground_truth.step_labels[step] = 1\n\n # 先预存正样本集合\n positive_preds = [self.ground_truth]\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n positive_preds.append(pred)\n\n # 将所有待NLI的文本预存起来\n self.collect_entailment_texts(positive_preds)\n\n # print(\"Number of entailment result keys:\", len(self.entailment_results.keys()))\n\n # 预处理所有NLI结果\n self.preprocess_entailment(model=model, tokenizer=tokenizer)\n\n # 再对所有样本的所有step打标签\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n pred.is_correct = True\n for step in pred.steps:\n pred.step_labels[step] = 1\n else:\n for k, step in enumerate(pred.steps):\n ans = TextEntailmentExample.match(\n pred.steps[:k+1],\n positive_preds,\n model=model,\n tokenizer=tokenizer,\n entailment_result_dict=self.entailment_results,\n )\n pred.step_labels[step] = ans\n \n def collect_entailment_texts(self, positive_preds):\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n pass\n else:\n for pp in positive_preds:\n for k, step in enumerate(pred.steps):\n if k >= len(pp.steps):\n continue\n pp_step = pp.steps[k].strip()\n text1 = f\"premise: {pp_step} hypothesis: {step}\"\n text2 = f\"premise: {step} hypothesis: {pp_step}\"\n self.entailment_results[text1] = -1\n self.entailment_results[text2] = -1\n \n def preprocess_entailment(self, model, tokenizer):\n text_all = list(self.entailment_results.keys())\n text_batch, results_batch = [], []\n for i in range(0, len(text_all), self.entailment_batch_size):","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.collect_entailment_texts","uri":"program://CodeT/function/DIVERSE.code.src.utils.collect_entailment_texts#L104-L117","kind":"function","name":"collect_entailment_texts","path":"DIVERSE/code/src/utils.py","language":"python","start_line":104,"end_line":117,"context_start_line":84,"context_end_line":137,"code":" # 预处理所有NLI结果\n self.preprocess_entailment(model=model, tokenizer=tokenizer)\n\n # 再对所有样本的所有step打标签\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n pred.is_correct = True\n for step in pred.steps:\n pred.step_labels[step] = 1\n else:\n for k, step in enumerate(pred.steps):\n ans = TextEntailmentExample.match(\n pred.steps[:k+1],\n positive_preds,\n model=model,\n tokenizer=tokenizer,\n entailment_result_dict=self.entailment_results,\n )\n pred.step_labels[step] = ans\n \n def collect_entailment_texts(self, positive_preds):\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n pass\n else:\n for pp in positive_preds:\n for k, step in enumerate(pred.steps):\n if k >= len(pp.steps):\n continue\n pp_step = pp.steps[k].strip()\n text1 = f\"premise: {pp_step} hypothesis: {step}\"\n text2 = f\"premise: {step} hypothesis: {pp_step}\"\n self.entailment_results[text1] = -1\n self.entailment_results[text2] = -1\n \n def preprocess_entailment(self, model, tokenizer):\n text_all = list(self.entailment_results.keys())\n text_batch, results_batch = [], []\n for i in range(0, len(text_all), self.entailment_batch_size):\n text_batch = text_all[i : min(len(text_all), i + self.entailment_batch_size)]\n batch_results = entailment_batch(text_batch, model, tokenizer)\n for sc in batch_results:\n results_batch.append(sc)\n for text, result in zip(text_batch, results_batch):\n self.entailment_results[text] = 1 if result else 0\n\n\nclass BaseExample:\n inf = \"-99999999\"\n \n def __init__(self, content):\n self.content = content.strip()\n self.steps = self.get_steps()\n self.step_labels = {}","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.preprocess_entailment","uri":"program://CodeT/function/DIVERSE.code.src.utils.preprocess_entailment#L119-L128","kind":"function","name":"preprocess_entailment","path":"DIVERSE/code/src/utils.py","language":"python","start_line":119,"end_line":128,"context_start_line":99,"context_end_line":148,"code":" tokenizer=tokenizer,\n entailment_result_dict=self.entailment_results,\n )\n pred.step_labels[step] = ans\n \n def collect_entailment_texts(self, positive_preds):\n for i, pred in enumerate(self.preds):\n if pred.get_final_answer() != BaseExample.inf and pred.get_final_answer() == self.ground_truth.get_final_answer():\n pass\n else:\n for pp in positive_preds:\n for k, step in enumerate(pred.steps):\n if k >= len(pp.steps):\n continue\n pp_step = pp.steps[k].strip()\n text1 = f\"premise: {pp_step} hypothesis: {step}\"\n text2 = f\"premise: {step} hypothesis: {pp_step}\"\n self.entailment_results[text1] = -1\n self.entailment_results[text2] = -1\n \n def preprocess_entailment(self, model, tokenizer):\n text_all = list(self.entailment_results.keys())\n text_batch, results_batch = [], []\n for i in range(0, len(text_all), self.entailment_batch_size):\n text_batch = text_all[i : min(len(text_all), i + self.entailment_batch_size)]\n batch_results = entailment_batch(text_batch, model, tokenizer)\n for sc in batch_results:\n results_batch.append(sc)\n for text, result in zip(text_batch, results_batch):\n self.entailment_results[text] = 1 if result else 0\n\n\nclass BaseExample:\n inf = \"-99999999\"\n \n def __init__(self, content):\n self.content = content.strip()\n self.steps = self.get_steps()\n self.step_labels = {}\n self.sequence_labels = []\n self.is_correct= False\n\n # Only for GSM8K dataset use\n def init_equations(self):\n raise NotImplementedError\n\n def get_steps(self):\n return [x+\"%%\" if x != self.content.split(\"%%\")[-1] else x for i, x in enumerate(self.content.split(\"%%\"))]\n\n def get_final_answer(self):","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.init_equations","uri":"program://CodeT/function/DIVERSE.code.src.utils.init_equations#L167-L168","kind":"function","name":"init_equations","path":"DIVERSE/code/src/utils.py","language":"python","start_line":167,"end_line":168,"context_start_line":147,"context_end_line":188,"code":"\n def get_final_answer(self):\n ans = \"\"\n if \"####\" in self.content:\n ans = self.content.split(\"####\")[-1].strip().replace(\"%%\", \"\").replace(\" \", \"\")\n else:\n ans = BaseExample.inf\n return clean_ans(ans)\n\n def label_to_string(self):\n return \"\".join(str(self.labels[k]) for k in self.labels.keys())\n\n\nclass GSM8KExample(BaseExample):\n def __init__(self, content):\n super().__init__(content)\n self.equations = self.init_equations()\n self.verifier_score = 0.0\n\n # 按'<>'的格式将公式提取出来\n def init_equations(self):\n return [x for x in re.findall(\"<<.+>>[0-9\\.]+\", self.content) if \"=\" in x]\n\n def get_step_answer(step):\n expression = re.findall(\"<<.+>>[0-9\\.]+\", step)\n if len(expression == 0):\n ans = BaseExample.inf\n else:\n ans = expression[-1].split(\">>\")[-1].strip()\n return clean_ans(ans)\n \n @staticmethod\n @lru_cache(maxsize=4096)\n def get_answer(s):\n ans = \"\"\n if \"####\" in s:\n ans = s.split(\"####\")[-1].replace(\"%%\", \"\").replace(\" \", \"\").strip()\n else:\n expression = re.findall(\"<<.+>>[0-9\\.]+\", s)\n if len(expression) == 0:\n ans = GSM8KExample.inf\n else:","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.get_steps","uri":"program://CodeT/function/DIVERSE.code.src.utils.get_steps#L145-L146","kind":"function","name":"get_steps","path":"DIVERSE/code/src/utils.py","language":"python","start_line":145,"end_line":146,"context_start_line":125,"context_end_line":166,"code":" for sc in batch_results:\n results_batch.append(sc)\n for text, result in zip(text_batch, results_batch):\n self.entailment_results[text] = 1 if result else 0\n\n\nclass BaseExample:\n inf = \"-99999999\"\n \n def __init__(self, content):\n self.content = content.strip()\n self.steps = self.get_steps()\n self.step_labels = {}\n self.sequence_labels = []\n self.is_correct= False\n\n # Only for GSM8K dataset use\n def init_equations(self):\n raise NotImplementedError\n\n def get_steps(self):\n return [x+\"%%\" if x != self.content.split(\"%%\")[-1] else x for i, x in enumerate(self.content.split(\"%%\"))]\n\n def get_final_answer(self):\n ans = \"\"\n if \"####\" in self.content:\n ans = self.content.split(\"####\")[-1].strip().replace(\"%%\", \"\").replace(\" \", \"\")\n else:\n ans = BaseExample.inf\n return clean_ans(ans)\n\n def label_to_string(self):\n return \"\".join(str(self.labels[k]) for k in self.labels.keys())\n\n\nclass GSM8KExample(BaseExample):\n def __init__(self, content):\n super().__init__(content)\n self.equations = self.init_equations()\n self.verifier_score = 0.0\n\n # 按'<>'的格式将公式提取出来","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.get_final_answer","uri":"program://CodeT/function/DIVERSE.code.src.utils.get_final_answer#L148-L154","kind":"function","name":"get_final_answer","path":"DIVERSE/code/src/utils.py","language":"python","start_line":148,"end_line":154,"context_start_line":128,"context_end_line":174,"code":" self.entailment_results[text] = 1 if result else 0\n\n\nclass BaseExample:\n inf = \"-99999999\"\n \n def __init__(self, content):\n self.content = content.strip()\n self.steps = self.get_steps()\n self.step_labels = {}\n self.sequence_labels = []\n self.is_correct= False\n\n # Only for GSM8K dataset use\n def init_equations(self):\n raise NotImplementedError\n\n def get_steps(self):\n return [x+\"%%\" if x != self.content.split(\"%%\")[-1] else x for i, x in enumerate(self.content.split(\"%%\"))]\n\n def get_final_answer(self):\n ans = \"\"\n if \"####\" in self.content:\n ans = self.content.split(\"####\")[-1].strip().replace(\"%%\", \"\").replace(\" \", \"\")\n else:\n ans = BaseExample.inf\n return clean_ans(ans)\n\n def label_to_string(self):\n return \"\".join(str(self.labels[k]) for k in self.labels.keys())\n\n\nclass GSM8KExample(BaseExample):\n def __init__(self, content):\n super().__init__(content)\n self.equations = self.init_equations()\n self.verifier_score = 0.0\n\n # 按'<>'的格式将公式提取出来\n def init_equations(self):\n return [x for x in re.findall(\"<<.+>>[0-9\\.]+\", self.content) if \"=\" in x]\n\n def get_step_answer(step):\n expression = re.findall(\"<<.+>>[0-9\\.]+\", step)\n if len(expression == 0):\n ans = BaseExample.inf\n else:","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.label_to_string","uri":"program://CodeT/function/DIVERSE.code.src.utils.label_to_string#L156-L157","kind":"function","name":"label_to_string","path":"DIVERSE/code/src/utils.py","language":"python","start_line":156,"end_line":157,"context_start_line":136,"context_end_line":177,"code":" self.steps = self.get_steps()\n self.step_labels = {}\n self.sequence_labels = []\n self.is_correct= False\n\n # Only for GSM8K dataset use\n def init_equations(self):\n raise NotImplementedError\n\n def get_steps(self):\n return [x+\"%%\" if x != self.content.split(\"%%\")[-1] else x for i, x in enumerate(self.content.split(\"%%\"))]\n\n def get_final_answer(self):\n ans = \"\"\n if \"####\" in self.content:\n ans = self.content.split(\"####\")[-1].strip().replace(\"%%\", \"\").replace(\" \", \"\")\n else:\n ans = BaseExample.inf\n return clean_ans(ans)\n\n def label_to_string(self):\n return \"\".join(str(self.labels[k]) for k in self.labels.keys())\n\n\nclass GSM8KExample(BaseExample):\n def __init__(self, content):\n super().__init__(content)\n self.equations = self.init_equations()\n self.verifier_score = 0.0\n\n # 按'<>'的格式将公式提取出来\n def init_equations(self):\n return [x for x in re.findall(\"<<.+>>[0-9\\.]+\", self.content) if \"=\" in x]\n\n def get_step_answer(step):\n expression = re.findall(\"<<.+>>[0-9\\.]+\", step)\n if len(expression == 0):\n ans = BaseExample.inf\n else:\n ans = expression[-1].split(\">>\")[-1].strip()\n return clean_ans(ans)\n ","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.get_step_answer","uri":"program://CodeT/function/DIVERSE.code.src.utils.get_step_answer#L170-L176","kind":"function","name":"get_step_answer","path":"DIVERSE/code/src/utils.py","language":"python","start_line":170,"end_line":176,"context_start_line":150,"context_end_line":196,"code":" if \"####\" in self.content:\n ans = self.content.split(\"####\")[-1].strip().replace(\"%%\", \"\").replace(\" \", \"\")\n else:\n ans = BaseExample.inf\n return clean_ans(ans)\n\n def label_to_string(self):\n return \"\".join(str(self.labels[k]) for k in self.labels.keys())\n\n\nclass GSM8KExample(BaseExample):\n def __init__(self, content):\n super().__init__(content)\n self.equations = self.init_equations()\n self.verifier_score = 0.0\n\n # 按'<>'的格式将公式提取出来\n def init_equations(self):\n return [x for x in re.findall(\"<<.+>>[0-9\\.]+\", self.content) if \"=\" in x]\n\n def get_step_answer(step):\n expression = re.findall(\"<<.+>>[0-9\\.]+\", step)\n if len(expression == 0):\n ans = BaseExample.inf\n else:\n ans = expression[-1].split(\">>\")[-1].strip()\n return clean_ans(ans)\n \n @staticmethod\n @lru_cache(maxsize=4096)\n def get_answer(s):\n ans = \"\"\n if \"####\" in s:\n ans = s.split(\"####\")[-1].replace(\"%%\", \"\").replace(\" \", \"\").strip()\n else:\n expression = re.findall(\"<<.+>>[0-9\\.]+\", s)\n if len(expression) == 0:\n ans = GSM8KExample.inf\n else:\n ans = expression[-1].split(\">>\")[-1].strip()\n return clean_ans(ans)\n \n @staticmethod\n def match(steps, positive_examples, model=None, tokenizer=None):\n curr_set = Multiset([GSM8KExample.get_answer(x) for x in steps])\n for positive_example in positive_examples:\n golden_set = Multiset([GSM8KExample.get_answer(x) for x in positive_example.steps])","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.get_answer","uri":"program://CodeT/function/DIVERSE.code.src.utils.get_answer#L180-L190","kind":"function","name":"get_answer","path":"DIVERSE/code/src/utils.py","language":"python","start_line":180,"end_line":190,"context_start_line":160,"context_end_line":210,"code":"class GSM8KExample(BaseExample):\n def __init__(self, content):\n super().__init__(content)\n self.equations = self.init_equations()\n self.verifier_score = 0.0\n\n # 按'<>'的格式将公式提取出来\n def init_equations(self):\n return [x for x in re.findall(\"<<.+>>[0-9\\.]+\", self.content) if \"=\" in x]\n\n def get_step_answer(step):\n expression = re.findall(\"<<.+>>[0-9\\.]+\", step)\n if len(expression == 0):\n ans = BaseExample.inf\n else:\n ans = expression[-1].split(\">>\")[-1].strip()\n return clean_ans(ans)\n \n @staticmethod\n @lru_cache(maxsize=4096)\n def get_answer(s):\n ans = \"\"\n if \"####\" in s:\n ans = s.split(\"####\")[-1].replace(\"%%\", \"\").replace(\" \", \"\").strip()\n else:\n expression = re.findall(\"<<.+>>[0-9\\.]+\", s)\n if len(expression) == 0:\n ans = GSM8KExample.inf\n else:\n ans = expression[-1].split(\">>\")[-1].strip()\n return clean_ans(ans)\n \n @staticmethod\n def match(steps, positive_examples, model=None, tokenizer=None):\n curr_set = Multiset([GSM8KExample.get_answer(x) for x in steps])\n for positive_example in positive_examples:\n golden_set = Multiset([GSM8KExample.get_answer(x) for x in positive_example.steps])\n if GSM8KExample.inf in curr_set:\n curr_set.remove(GSM8KExample.inf)\n if GSM8KExample.inf in golden_set:\n golden_set.remove(GSM8KExample.inf)\n if len(curr_set) == 0:\n return 0\n if curr_set.issubset(golden_set):\n return 1\n return 0\n \n def get_sequence_labels(question, pred):\n sequence_labels = []\n if pred.is_correct:\n sequence_labels.append((\"[CLS]\", \"SOLUTION-CORRECT\"))","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.match","uri":"program://CodeT/function/DIVERSE.code.src.utils.match#L241-L245","kind":"function","name":"match","path":"DIVERSE/code/src/utils.py","language":"python","start_line":241,"end_line":245,"context_start_line":221,"context_end_line":265,"code":" else:\n sequence_labels.append((token, \"STEP-INCORRECT\"))\n else:\n sequence_labels.append((token, \"O\"))\n\n # add a split symbol\n sequence_labels.append((\"&&\", \"O\"))\n\n # add question tokens\n for token in question.split(\" \"):\n sequence_labels.append((token, \"O\"))\n\n return sequence_labels\n \n\nclass TextEntailmentExample(BaseExample):\n def __init__(self, content):\n super().__init__(content)\n\n @staticmethod\n def match(steps, positive_examples, model, tokenizer, entailment_result_dict):\n for pp in positive_examples:\n if TextEntailmentExample.match_per_example(pp, steps, entailment_result_dict):\n return 1\n return 0\n \n @staticmethod\n def match_per_example(pp, steps, entailment_result_dict):\n for k, step in enumerate(steps):\n if k >= len(pp.steps):\n continue\n # print(\"step:\", step)\n # print(\"pp.steps[k]:\", pp.steps[k])\n pp_step = pp.steps[k].strip()\n text1 = f\"premise: {step} hypothesis: {pp_step}\"\n text2 = f\"premise: {pp_step} hypothesis: {step}\"\n if entailment_result_dict[text1] == 0 or entailment_result_dict[text2] == 0:\n # error_case = 'No, Christmas trees are not dissimilar to deciduous trees.%%Both Christmas trees and deciduous trees are types of trees.%%Both Christmas trees and deciduous trees have leaves.%%So the answer is no.#### no'\n # if error_case in text1 or error_case in text2:\n # print(\"text1:\", text1)\n # print(\"text2:\", text2)\n # pdb.set_trace()\n return 0\n return 1\n","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.get_sequence_labels","uri":"program://CodeT/function/DIVERSE.code.src.utils.get_sequence_labels#L266-L294","kind":"function","name":"get_sequence_labels","path":"DIVERSE/code/src/utils.py","language":"python","start_line":266,"end_line":294,"context_start_line":246,"context_end_line":314,"code":" \n @staticmethod\n def match_per_example(pp, steps, entailment_result_dict):\n for k, step in enumerate(steps):\n if k >= len(pp.steps):\n continue\n # print(\"step:\", step)\n # print(\"pp.steps[k]:\", pp.steps[k])\n pp_step = pp.steps[k].strip()\n text1 = f\"premise: {step} hypothesis: {pp_step}\"\n text2 = f\"premise: {pp_step} hypothesis: {step}\"\n if entailment_result_dict[text1] == 0 or entailment_result_dict[text2] == 0:\n # error_case = 'No, Christmas trees are not dissimilar to deciduous trees.%%Both Christmas trees and deciduous trees are types of trees.%%Both Christmas trees and deciduous trees have leaves.%%So the answer is no.#### no'\n # if error_case in text1 or error_case in text2:\n # print(\"text1:\", text1)\n # print(\"text2:\", text2)\n # pdb.set_trace()\n return 0\n return 1\n\n def get_sequence_labels(question, pred):\n sequence_labels = []\n if pred.is_correct:\n sequence_labels.append((\"[CLS]\", \"SOLUTION-CORRECT\"))\n else:\n sequence_labels.append((\"[CLS]\", \"SOLUTION-INCORRECT\"))\n\n # add step tokens\n for s in pred.steps:\n token_list = [x for x in re.split(\"(%%| )\", s) if x != ' ']\n for token in token_list:\n if token == \"\":\n continue\n if token == \"%%\":\n if pred.step_labels[s] == 1:\n sequence_labels.append((token, \"STEP-CORRECT\"))\n else:\n sequence_labels.append((token, \"STEP-INCORRECT\"))\n else:\n sequence_labels.append((token, \"O\"))\n\n # add a split symbol\n sequence_labels.append((\"&&\", \"O\"))\n\n # add question tokens\n for token in question.split(\" \"):\n sequence_labels.append((token, \"O\"))\n\n return sequence_labels\n\n\n@torch.no_grad()\ndef entailment_batch(text, model, tokenizer):\n inputs = tokenizer(text, padding=True, truncation=True, return_tensors=\"pt\").to(\"cuda\")\n labels = torch.tensor([1] * len(text)).to(\"cuda\")\n outputs = model(**inputs, labels=labels)\n logits = outputs.logits\n ans_list = torch.argmax(F.softmax(logits, dim=-1), dim=-1).tolist()\n ans_list = [x == model.config.label2id[\"ENTAILMENT\"] for x in ans_list]\n return ans_list\n\n\n@torch.no_grad()\ndef entailment(premise, hypothesis, model, tokenizer):\n text = f\"premise: {premise} hypothesis: {hypothesis}\"\n inputs = tokenizer(text, padding=True, truncation=True, return_tensors=\"pt\").to(model.device)\n labels = torch.tensor([1]).to(model.device)\n outputs = model(**inputs, labels=labels)\n logits = outputs.logits","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils.match_per_example","uri":"program://CodeT/function/DIVERSE.code.src.utils.match_per_example#L248-L264","kind":"function","name":"match_per_example","path":"DIVERSE/code/src/utils.py","language":"python","start_line":248,"end_line":264,"context_start_line":228,"context_end_line":284,"code":"\n # add question tokens\n for token in question.split(\" \"):\n sequence_labels.append((token, \"O\"))\n\n return sequence_labels\n \n\nclass TextEntailmentExample(BaseExample):\n def __init__(self, content):\n super().__init__(content)\n\n @staticmethod\n def match(steps, positive_examples, model, tokenizer, entailment_result_dict):\n for pp in positive_examples:\n if TextEntailmentExample.match_per_example(pp, steps, entailment_result_dict):\n return 1\n return 0\n \n @staticmethod\n def match_per_example(pp, steps, entailment_result_dict):\n for k, step in enumerate(steps):\n if k >= len(pp.steps):\n continue\n # print(\"step:\", step)\n # print(\"pp.steps[k]:\", pp.steps[k])\n pp_step = pp.steps[k].strip()\n text1 = f\"premise: {step} hypothesis: {pp_step}\"\n text2 = f\"premise: {pp_step} hypothesis: {step}\"\n if entailment_result_dict[text1] == 0 or entailment_result_dict[text2] == 0:\n # error_case = 'No, Christmas trees are not dissimilar to deciduous trees.%%Both Christmas trees and deciduous trees are types of trees.%%Both Christmas trees and deciduous trees have leaves.%%So the answer is no.#### no'\n # if error_case in text1 or error_case in text2:\n # print(\"text1:\", text1)\n # print(\"text2:\", text2)\n # pdb.set_trace()\n return 0\n return 1\n\n def get_sequence_labels(question, pred):\n sequence_labels = []\n if pred.is_correct:\n sequence_labels.append((\"[CLS]\", \"SOLUTION-CORRECT\"))\n else:\n sequence_labels.append((\"[CLS]\", \"SOLUTION-INCORRECT\"))\n\n # add step tokens\n for s in pred.steps:\n token_list = [x for x in re.split(\"(%%| )\", s) if x != ' ']\n for token in token_list:\n if token == \"\":\n continue\n if token == \"%%\":\n if pred.step_labels[s] == 1:\n sequence_labels.append((token, \"STEP-CORRECT\"))\n else:\n sequence_labels.append((token, \"STEP-INCORRECT\"))\n else:","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner","uri":"program://CodeT/module/DIVERSE.code.src.utils_ner#L1-L395","kind":"module","name":"DIVERSE.code.src.utils_ner","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":1,"end_line":395,"context_start_line":1,"context_end_line":395,"code":"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" Named entity recognition fine-tuning: utilities to work with CoNLL-2003 task. \"\"\"\n\n\nimport logging\nimport os\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom typing import List, Optional, Union\n\nfrom filelock import FileLock\nfrom transformers import PreTrainedTokenizer, is_tf_available, is_torch_available\nimport pdb\n\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass InputExample:\n \"\"\"\n A single training/test example for token classification.\n\n Args:\n guid: Unique id for the example.\n words: list. The words of the sequence.\n labels: (Optional) list. The labels for each word of the sequence. This should be\n specified for train and dev examples, but not for test examples.\n \"\"\"\n\n guid: str\n words: List[str]\n labels: Optional[List[str]]\n\n\n@dataclass\nclass InputFeatures:\n \"\"\"\n A single set of features of data.\n Property names are the same names as the corresponding inputs to a model.\n \"\"\"\n\n input_ids: List[int]\n attention_mask: List[int]\n token_type_ids: Optional[List[int]] = None\n label_ids: Optional[List[int]] = None\n\n\nclass Split(Enum):\n train = \"train\"\n dev = \"dev\"\n test = \"test\"\n\n\nclass TokenClassificationTask:\n @staticmethod\n def read_examples_from_file(data_dir, mode: Union[Split, str]) -> List[InputExample]:\n raise NotImplementedError\n\n @staticmethod\n def get_labels(path: str) -> List[str]:\n raise NotImplementedError\n\n @staticmethod\n def check_placeholder_pattern(example):\n placeholder_patterns = [\n \"This is a placeholder ####\",\n \"No chain-of-thought provided\"\n ]\n for patt in placeholder_patterns:\n if patt in example:\n return True\n return False\n\n @staticmethod\n def convert_examples_to_features(\n examples: List[InputExample],\n label_list: List[str],\n max_seq_length: int,\n tokenizer: PreTrainedTokenizer,\n cls_token_at_end=False,\n cls_token=\"[CLS]\",\n cls_token_segment_id=1,\n sep_token=\"[SEP]\",\n sep_token_extra=False,\n pad_on_left=False,\n pad_token=0,\n pad_token_segment_id=0,\n pad_token_label_id=-100,\n sequence_a_segment_id=0,\n mask_padding_with_zero=True,\n mode: Split = Split.dev\n ) -> List[InputFeatures]:\n \"\"\"Loads a data file into a list of `InputFeatures`\n `cls_token_at_end` define the location of the CLS token:\n - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]\n - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]\n `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)\n \"\"\"\n # TODO clean up all this to leverage built-in features of tokenizers\n\n label_map = {label: i for i, label in enumerate(label_list)}\n label_map[\"O\"] = pad_token_label_id\n\n features = []\n for (ex_index, example) in enumerate(examples):\n if ex_index % 10_000 == 0:\n logger.info(\"Writing example %d of %d\", ex_index, len(examples))\n\n # skip the placeholder examples\n example_str = \" \".join(example.words)\n if ex_index == 0:\n print(example_str)\n if mode == Split.train and TokenClassificationTask.check_placeholder_pattern(example_str):\n continue\n\n tokens = []\n label_ids = []\n for word, label in zip(example.words, example.labels):\n word_tokens = tokenizer.tokenize(word)\n\n # bert-base-multilingual-cased sometimes output \"nothing ([]) when calling tokenize with just a space.\n if len(word_tokens) > 0:\n tokens.extend(word_tokens)\n # Use the real label id for the first token of the word, and padding ids for the remaining tokens\n label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))\n\n # Account for [CLS] and [SEP] with \"- 2\" and with \"- 3\" for RoBERTa.\n special_tokens_count = tokenizer.num_special_tokens_to_add()\n if len(tokens) > max_seq_length - special_tokens_count:\n tokens = tokens[: (max_seq_length - special_tokens_count)]\n label_ids = label_ids[: (max_seq_length - special_tokens_count)]\n\n # The convention in BERT is:\n # (a) For sequence pairs:\n # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]\n # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1\n # (b) For single sequences:\n # tokens: [CLS] the dog is hairy . [SEP]\n # type_ids: 0 0 0 0 0 0 0\n #\n # Where \"type_ids\" are used to indicate whether this is the first\n # sequence or the second sequence. The embedding vectors for `type=0` and\n # `type=1` were learned during pre-training and are added to the wordpiece\n # embedding vector (and position vector). This is not *strictly* necessary\n # since the [SEP] token unambiguously separates the sequences, but it makes\n # it easier for the model to learn the concept of sequences.\n #\n # For classification tasks, the first vector (corresponding to [CLS]) is\n # used as as the \"sentence vector\". Note that this only makes sense because\n # the entire model is fine-tuned.\n tokens += [sep_token]\n label_ids += [pad_token_label_id]\n if sep_token_extra:\n # roberta uses an extra separator b/w pairs of sentences\n tokens += [sep_token]\n label_ids += [pad_token_label_id]\n segment_ids = [sequence_a_segment_id] * len(tokens)\n\n if cls_token_at_end:\n tokens += [cls_token]\n label_ids += [pad_token_label_id]\n segment_ids += [cls_token_segment_id]\n else:\n tokens = [cls_token] + tokens\n label_ids = [pad_token_label_id] + label_ids\n segment_ids = [cls_token_segment_id] + segment_ids\n\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\n\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\n # tokens are attended to.\n input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)\n\n # Zero-pad up to the sequence length.\n padding_length = max_seq_length - len(input_ids)\n if pad_on_left:\n input_ids = ([pad_token] * padding_length) + input_ids\n input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask\n segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids\n label_ids = ([pad_token_label_id] * padding_length) + label_ids\n else:\n input_ids += [pad_token] * padding_length\n input_mask += [0 if mask_padding_with_zero else 1] * padding_length\n segment_ids += [pad_token_segment_id] * padding_length\n label_ids += [pad_token_label_id] * padding_length\n\n assert len(input_ids) == max_seq_length\n assert len(input_mask) == max_seq_length\n assert len(segment_ids) == max_seq_length\n assert len(label_ids) == max_seq_length\n\n if ex_index < 5:\n logger.info(\"*** Example ***\")\n logger.info(\"guid: %s\", example.guid)\n logger.info(\"tokens: %s\", \" \".join([str(x) for x in tokens]))\n logger.info(\"input_ids: %s\", \" \".join([str(x) for x in input_ids]))\n logger.info(\"input_mask: %s\", \" \".join([str(x) for x in input_mask]))\n logger.info(\"segment_ids: %s\", \" \".join([str(x) for x in segment_ids]))\n logger.info(\"label_ids: %s\", \" \".join([str(x) for x in label_ids]))\n\n if \"token_type_ids\" not in tokenizer.model_input_names:\n segment_ids = None\n\n features.append(\n InputFeatures(\n input_ids=input_ids, attention_mask=input_mask, token_type_ids=segment_ids, label_ids=label_ids\n )\n )\n return features\n\n\nif is_torch_available():\n import torch\n from torch import nn\n from torch.utils.data import Dataset\n\n class TokenClassificationDataset(Dataset):\n \"\"\"\n This will be superseded by a framework-agnostic approach\n soon.\n \"\"\"\n\n features: List[InputFeatures]\n pad_token_label_id: int = nn.CrossEntropyLoss().ignore_index\n # Use cross entropy ignore_index as padding label id so that only\n # real label ids contribute to the loss later.\n\n def __init__(\n self,\n token_classification_task: TokenClassificationTask,\n data_dir: str,\n tokenizer: PreTrainedTokenizer,\n labels: List[str],\n model_type: str,\n max_seq_length: Optional[int] = None,\n overwrite_cache=False,\n mode: Split = Split.train,\n ):\n # Load data features from cache or dataset file\n cached_features_file = os.path.join(\n data_dir,\n \"cached_{}_{}_{}\".format(mode.value, tokenizer.__class__.__name__, str(max_seq_length)),\n )\n\n # Make sure only the first process in distributed training processes the dataset,\n # and the others will use the cache.\n lock_path = cached_features_file + \".lock\"\n with FileLock(lock_path):\n\n if os.path.exists(cached_features_file) and not overwrite_cache:\n logger.error(f\"Loading features from cached file {cached_features_file}\")\n self.features = torch.load(cached_features_file)\n else:\n logger.error(f\"Creating features from dataset file at {data_dir}\")\n examples = token_classification_task.read_examples_from_file(data_dir, mode)\n # TODO clean up all this to leverage built-in features of tokenizers\n self.features = token_classification_task.convert_examples_to_features(\n examples,\n labels,\n max_seq_length,\n tokenizer,\n cls_token_at_end=bool(model_type in [\"xlnet\"]),\n # xlnet has a cls token at the end\n cls_token=tokenizer.cls_token,\n cls_token_segment_id=2 if model_type in [\"xlnet\"] else 0,\n sep_token=tokenizer.sep_token,\n sep_token_extra=False,\n # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805\n pad_on_left=bool(tokenizer.padding_side == \"left\"),\n pad_token=tokenizer.pad_token_id,\n pad_token_segment_id=tokenizer.pad_token_type_id,\n pad_token_label_id=self.pad_token_label_id,\n mode = mode\n )\n logger.error(f\"Saving features into cached file {cached_features_file}\")\n torch.save(self.features, cached_features_file)\n\n def __len__(self):\n return len(self.features)\n\n def __getitem__(self, i) -> InputFeatures:\n return self.features[i]\n\n\nif is_tf_available():\n import tensorflow as tf\n\n class TFTokenClassificationDataset:\n \"\"\"\n This will be superseded by a framework-agnostic approach\n soon.\n \"\"\"\n\n features: List[InputFeatures]\n pad_token_label_id: int = -100\n # Use cross entropy ignore_index as padding label id so that only\n # real label ids contribute to the loss later.\n\n def __init__(\n self,\n token_classification_task: TokenClassificationTask,\n data_dir: str,\n tokenizer: PreTrainedTokenizer,\n labels: List[str],\n model_type: str,\n max_seq_length: Optional[int] = None,\n overwrite_cache=False,\n mode: Split = Split.train,\n ):\n examples = token_classification_task.read_examples_from_file(data_dir, mode)\n # TODO clean up all this to leverage built-in features of tokenizers\n self.features = token_classification_task.convert_examples_to_features(\n examples,\n labels,\n max_seq_length,\n tokenizer,\n cls_token_at_end=bool(model_type in [\"xlnet\"]),\n # xlnet has a cls token at the end\n cls_token=tokenizer.cls_token,\n cls_token_segment_id=2 if model_type in [\"xlnet\"] else 0,\n sep_token=tokenizer.sep_token,\n sep_token_extra=False,\n # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805\n pad_on_left=bool(tokenizer.padding_side == \"left\"),\n pad_token=tokenizer.pad_token_id,\n pad_token_segment_id=tokenizer.pad_token_type_id,\n pad_token_label_id=self.pad_token_label_id,\n mode = mode\n )\n\n def gen():\n for ex in self.features:\n if ex.token_type_ids is None:\n yield (\n {\"input_ids\": ex.input_ids, \"attention_mask\": ex.attention_mask},\n ex.label_ids,\n )\n else:\n yield (\n {\n \"input_ids\": ex.input_ids,\n \"attention_mask\": ex.attention_mask,\n \"token_type_ids\": ex.token_type_ids,\n },\n ex.label_ids,\n )\n\n if \"token_type_ids\" not in tokenizer.model_input_names:\n self.dataset = tf.data.Dataset.from_generator(\n gen,\n ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32}, tf.int64),\n (\n {\"input_ids\": tf.TensorShape([None]), \"attention_mask\": tf.TensorShape([None])},\n tf.TensorShape([None]),\n ),\n )\n else:\n self.dataset = tf.data.Dataset.from_generator(\n gen,\n ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32, \"token_type_ids\": tf.int32}, tf.int64),\n (\n {\n \"input_ids\": tf.TensorShape([None]),\n \"attention_mask\": tf.TensorShape([None]),\n \"token_type_ids\": tf.TensorShape([None]),\n },\n tf.TensorShape([None]),\n ),\n )\n\n def get_dataset(self):\n self.dataset = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))\n\n return self.dataset\n\n def __len__(self):\n return len(self.features)\n\n def __getitem__(self, i) -> InputFeatures:\n return self.features[i]","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner.InputExample","uri":"program://CodeT/class/DIVERSE.code.src.utils_ner.InputExample#L34-L47","kind":"class","name":"InputExample","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":34,"end_line":47,"context_start_line":14,"context_end_line":67,"code":"# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" Named entity recognition fine-tuning: utilities to work with CoNLL-2003 task. \"\"\"\n\n\nimport logging\nimport os\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom typing import List, Optional, Union\n\nfrom filelock import FileLock\nfrom transformers import PreTrainedTokenizer, is_tf_available, is_torch_available\nimport pdb\n\n\nlogger = logging.getLogger(__name__)\n\n\n@dataclass\nclass InputExample:\n \"\"\"\n A single training/test example for token classification.\n\n Args:\n guid: Unique id for the example.\n words: list. The words of the sequence.\n labels: (Optional) list. The labels for each word of the sequence. This should be\n specified for train and dev examples, but not for test examples.\n \"\"\"\n\n guid: str\n words: List[str]\n labels: Optional[List[str]]\n\n\n@dataclass\nclass InputFeatures:\n \"\"\"\n A single set of features of data.\n Property names are the same names as the corresponding inputs to a model.\n \"\"\"\n\n input_ids: List[int]\n attention_mask: List[int]\n token_type_ids: Optional[List[int]] = None\n label_ids: Optional[List[int]] = None\n\n\nclass Split(Enum):\n train = \"train\"\n dev = \"dev\"\n test = \"test\"\n","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner.InputFeatures","uri":"program://CodeT/class/DIVERSE.code.src.utils_ner.InputFeatures#L51-L60","kind":"class","name":"InputFeatures","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":51,"end_line":60,"context_start_line":31,"context_end_line":80,"code":"\n\n@dataclass\nclass InputExample:\n \"\"\"\n A single training/test example for token classification.\n\n Args:\n guid: Unique id for the example.\n words: list. The words of the sequence.\n labels: (Optional) list. The labels for each word of the sequence. This should be\n specified for train and dev examples, but not for test examples.\n \"\"\"\n\n guid: str\n words: List[str]\n labels: Optional[List[str]]\n\n\n@dataclass\nclass InputFeatures:\n \"\"\"\n A single set of features of data.\n Property names are the same names as the corresponding inputs to a model.\n \"\"\"\n\n input_ids: List[int]\n attention_mask: List[int]\n token_type_ids: Optional[List[int]] = None\n label_ids: Optional[List[int]] = None\n\n\nclass Split(Enum):\n train = \"train\"\n dev = \"dev\"\n test = \"test\"\n\n\nclass TokenClassificationTask:\n @staticmethod\n def read_examples_from_file(data_dir, mode: Union[Split, str]) -> List[InputExample]:\n raise NotImplementedError\n\n @staticmethod\n def get_labels(path: str) -> List[str]:\n raise NotImplementedError\n\n @staticmethod\n def check_placeholder_pattern(example):\n placeholder_patterns = [","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner.Split","uri":"program://CodeT/class/DIVERSE.code.src.utils_ner.Split#L63-L66","kind":"class","name":"Split","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":63,"end_line":66,"context_start_line":43,"context_end_line":86,"code":" \"\"\"\n\n guid: str\n words: List[str]\n labels: Optional[List[str]]\n\n\n@dataclass\nclass InputFeatures:\n \"\"\"\n A single set of features of data.\n Property names are the same names as the corresponding inputs to a model.\n \"\"\"\n\n input_ids: List[int]\n attention_mask: List[int]\n token_type_ids: Optional[List[int]] = None\n label_ids: Optional[List[int]] = None\n\n\nclass Split(Enum):\n train = \"train\"\n dev = \"dev\"\n test = \"test\"\n\n\nclass TokenClassificationTask:\n @staticmethod\n def read_examples_from_file(data_dir, mode: Union[Split, str]) -> List[InputExample]:\n raise NotImplementedError\n\n @staticmethod\n def get_labels(path: str) -> List[str]:\n raise NotImplementedError\n\n @staticmethod\n def check_placeholder_pattern(example):\n placeholder_patterns = [\n \"This is a placeholder ####\",\n \"No chain-of-thought provided\"\n ]\n for patt in placeholder_patterns:\n if patt in example:\n return True","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner.TokenClassificationTask","uri":"program://CodeT/class/DIVERSE.code.src.utils_ner.TokenClassificationTask#L69-L224","kind":"class","name":"TokenClassificationTask","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":69,"end_line":224,"context_start_line":49,"context_end_line":244,"code":"\n@dataclass\nclass InputFeatures:\n \"\"\"\n A single set of features of data.\n Property names are the same names as the corresponding inputs to a model.\n \"\"\"\n\n input_ids: List[int]\n attention_mask: List[int]\n token_type_ids: Optional[List[int]] = None\n label_ids: Optional[List[int]] = None\n\n\nclass Split(Enum):\n train = \"train\"\n dev = \"dev\"\n test = \"test\"\n\n\nclass TokenClassificationTask:\n @staticmethod\n def read_examples_from_file(data_dir, mode: Union[Split, str]) -> List[InputExample]:\n raise NotImplementedError\n\n @staticmethod\n def get_labels(path: str) -> List[str]:\n raise NotImplementedError\n\n @staticmethod\n def check_placeholder_pattern(example):\n placeholder_patterns = [\n \"This is a placeholder ####\",\n \"No chain-of-thought provided\"\n ]\n for patt in placeholder_patterns:\n if patt in example:\n return True\n return False\n\n @staticmethod\n def convert_examples_to_features(\n examples: List[InputExample],\n label_list: List[str],\n max_seq_length: int,\n tokenizer: PreTrainedTokenizer,\n cls_token_at_end=False,\n cls_token=\"[CLS]\",\n cls_token_segment_id=1,\n sep_token=\"[SEP]\",\n sep_token_extra=False,\n pad_on_left=False,\n pad_token=0,\n pad_token_segment_id=0,\n pad_token_label_id=-100,\n sequence_a_segment_id=0,\n mask_padding_with_zero=True,\n mode: Split = Split.dev\n ) -> List[InputFeatures]:\n \"\"\"Loads a data file into a list of `InputFeatures`\n `cls_token_at_end` define the location of the CLS token:\n - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]\n - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]\n `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)\n \"\"\"\n # TODO clean up all this to leverage built-in features of tokenizers\n\n label_map = {label: i for i, label in enumerate(label_list)}\n label_map[\"O\"] = pad_token_label_id\n\n features = []\n for (ex_index, example) in enumerate(examples):\n if ex_index % 10_000 == 0:\n logger.info(\"Writing example %d of %d\", ex_index, len(examples))\n\n # skip the placeholder examples\n example_str = \" \".join(example.words)\n if ex_index == 0:\n print(example_str)\n if mode == Split.train and TokenClassificationTask.check_placeholder_pattern(example_str):\n continue\n\n tokens = []\n label_ids = []\n for word, label in zip(example.words, example.labels):\n word_tokens = tokenizer.tokenize(word)\n\n # bert-base-multilingual-cased sometimes output \"nothing ([]) when calling tokenize with just a space.\n if len(word_tokens) > 0:\n tokens.extend(word_tokens)\n # Use the real label id for the first token of the word, and padding ids for the remaining tokens\n label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))\n\n # Account for [CLS] and [SEP] with \"- 2\" and with \"- 3\" for RoBERTa.\n special_tokens_count = tokenizer.num_special_tokens_to_add()\n if len(tokens) > max_seq_length - special_tokens_count:\n tokens = tokens[: (max_seq_length - special_tokens_count)]\n label_ids = label_ids[: (max_seq_length - special_tokens_count)]\n\n # The convention in BERT is:\n # (a) For sequence pairs:\n # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]\n # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1\n # (b) For single sequences:\n # tokens: [CLS] the dog is hairy . [SEP]\n # type_ids: 0 0 0 0 0 0 0\n #\n # Where \"type_ids\" are used to indicate whether this is the first\n # sequence or the second sequence. The embedding vectors for `type=0` and\n # `type=1` were learned during pre-training and are added to the wordpiece\n # embedding vector (and position vector). This is not *strictly* necessary\n # since the [SEP] token unambiguously separates the sequences, but it makes\n # it easier for the model to learn the concept of sequences.\n #\n # For classification tasks, the first vector (corresponding to [CLS]) is\n # used as as the \"sentence vector\". Note that this only makes sense because\n # the entire model is fine-tuned.\n tokens += [sep_token]\n label_ids += [pad_token_label_id]\n if sep_token_extra:\n # roberta uses an extra separator b/w pairs of sentences\n tokens += [sep_token]\n label_ids += [pad_token_label_id]\n segment_ids = [sequence_a_segment_id] * len(tokens)\n\n if cls_token_at_end:\n tokens += [cls_token]\n label_ids += [pad_token_label_id]\n segment_ids += [cls_token_segment_id]\n else:\n tokens = [cls_token] + tokens\n label_ids = [pad_token_label_id] + label_ids\n segment_ids = [cls_token_segment_id] + segment_ids\n\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\n\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\n # tokens are attended to.\n input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)\n\n # Zero-pad up to the sequence length.\n padding_length = max_seq_length - len(input_ids)\n if pad_on_left:\n input_ids = ([pad_token] * padding_length) + input_ids\n input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask\n segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids\n label_ids = ([pad_token_label_id] * padding_length) + label_ids\n else:\n input_ids += [pad_token] * padding_length\n input_mask += [0 if mask_padding_with_zero else 1] * padding_length\n segment_ids += [pad_token_segment_id] * padding_length\n label_ids += [pad_token_label_id] * padding_length\n\n assert len(input_ids) == max_seq_length\n assert len(input_mask) == max_seq_length\n assert len(segment_ids) == max_seq_length\n assert len(label_ids) == max_seq_length\n\n if ex_index < 5:\n logger.info(\"*** Example ***\")\n logger.info(\"guid: %s\", example.guid)\n logger.info(\"tokens: %s\", \" \".join([str(x) for x in tokens]))\n logger.info(\"input_ids: %s\", \" \".join([str(x) for x in input_ids]))\n logger.info(\"input_mask: %s\", \" \".join([str(x) for x in input_mask]))\n logger.info(\"segment_ids: %s\", \" \".join([str(x) for x in segment_ids]))\n logger.info(\"label_ids: %s\", \" \".join([str(x) for x in label_ids]))\n\n if \"token_type_ids\" not in tokenizer.model_input_names:\n segment_ids = None\n\n features.append(\n InputFeatures(\n input_ids=input_ids, attention_mask=input_mask, token_type_ids=segment_ids, label_ids=label_ids\n )\n )\n return features\n\n\nif is_torch_available():\n import torch\n from torch import nn\n from torch.utils.data import Dataset\n\n class TokenClassificationDataset(Dataset):\n \"\"\"\n This will be superseded by a framework-agnostic approach\n soon.\n \"\"\"\n\n features: List[InputFeatures]\n pad_token_label_id: int = nn.CrossEntropyLoss().ignore_index\n # Use cross entropy ignore_index as padding label id so that only\n # real label ids contribute to the loss later.\n\n def __init__(\n self,","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner.read_examples_from_file","uri":"program://CodeT/function/DIVERSE.code.src.utils_ner.read_examples_from_file#L71-L72","kind":"function","name":"read_examples_from_file","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":71,"end_line":72,"context_start_line":51,"context_end_line":92,"code":"class InputFeatures:\n \"\"\"\n A single set of features of data.\n Property names are the same names as the corresponding inputs to a model.\n \"\"\"\n\n input_ids: List[int]\n attention_mask: List[int]\n token_type_ids: Optional[List[int]] = None\n label_ids: Optional[List[int]] = None\n\n\nclass Split(Enum):\n train = \"train\"\n dev = \"dev\"\n test = \"test\"\n\n\nclass TokenClassificationTask:\n @staticmethod\n def read_examples_from_file(data_dir, mode: Union[Split, str]) -> List[InputExample]:\n raise NotImplementedError\n\n @staticmethod\n def get_labels(path: str) -> List[str]:\n raise NotImplementedError\n\n @staticmethod\n def check_placeholder_pattern(example):\n placeholder_patterns = [\n \"This is a placeholder ####\",\n \"No chain-of-thought provided\"\n ]\n for patt in placeholder_patterns:\n if patt in example:\n return True\n return False\n\n @staticmethod\n def convert_examples_to_features(\n examples: List[InputExample],\n label_list: List[str],","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner.get_labels","uri":"program://CodeT/function/DIVERSE.code.src.utils_ner.get_labels#L75-L76","kind":"function","name":"get_labels","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":75,"end_line":76,"context_start_line":55,"context_end_line":96,"code":" \"\"\"\n\n input_ids: List[int]\n attention_mask: List[int]\n token_type_ids: Optional[List[int]] = None\n label_ids: Optional[List[int]] = None\n\n\nclass Split(Enum):\n train = \"train\"\n dev = \"dev\"\n test = \"test\"\n\n\nclass TokenClassificationTask:\n @staticmethod\n def read_examples_from_file(data_dir, mode: Union[Split, str]) -> List[InputExample]:\n raise NotImplementedError\n\n @staticmethod\n def get_labels(path: str) -> List[str]:\n raise NotImplementedError\n\n @staticmethod\n def check_placeholder_pattern(example):\n placeholder_patterns = [\n \"This is a placeholder ####\",\n \"No chain-of-thought provided\"\n ]\n for patt in placeholder_patterns:\n if patt in example:\n return True\n return False\n\n @staticmethod\n def convert_examples_to_features(\n examples: List[InputExample],\n label_list: List[str],\n max_seq_length: int,\n tokenizer: PreTrainedTokenizer,\n cls_token_at_end=False,\n cls_token=\"[CLS]\",","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner.check_placeholder_pattern","uri":"program://CodeT/function/DIVERSE.code.src.utils_ner.check_placeholder_pattern#L79-L87","kind":"function","name":"check_placeholder_pattern","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":79,"end_line":87,"context_start_line":59,"context_end_line":107,"code":" token_type_ids: Optional[List[int]] = None\n label_ids: Optional[List[int]] = None\n\n\nclass Split(Enum):\n train = \"train\"\n dev = \"dev\"\n test = \"test\"\n\n\nclass TokenClassificationTask:\n @staticmethod\n def read_examples_from_file(data_dir, mode: Union[Split, str]) -> List[InputExample]:\n raise NotImplementedError\n\n @staticmethod\n def get_labels(path: str) -> List[str]:\n raise NotImplementedError\n\n @staticmethod\n def check_placeholder_pattern(example):\n placeholder_patterns = [\n \"This is a placeholder ####\",\n \"No chain-of-thought provided\"\n ]\n for patt in placeholder_patterns:\n if patt in example:\n return True\n return False\n\n @staticmethod\n def convert_examples_to_features(\n examples: List[InputExample],\n label_list: List[str],\n max_seq_length: int,\n tokenizer: PreTrainedTokenizer,\n cls_token_at_end=False,\n cls_token=\"[CLS]\",\n cls_token_segment_id=1,\n sep_token=\"[SEP]\",\n sep_token_extra=False,\n pad_on_left=False,\n pad_token=0,\n pad_token_segment_id=0,\n pad_token_label_id=-100,\n sequence_a_segment_id=0,\n mask_padding_with_zero=True,\n mode: Split = Split.dev\n ) -> List[InputFeatures]:","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner.convert_examples_to_features","uri":"program://CodeT/function/DIVERSE.code.src.utils_ner.convert_examples_to_features#L90-L224","kind":"function","name":"convert_examples_to_features","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":90,"end_line":224,"context_start_line":70,"context_end_line":244,"code":" @staticmethod\n def read_examples_from_file(data_dir, mode: Union[Split, str]) -> List[InputExample]:\n raise NotImplementedError\n\n @staticmethod\n def get_labels(path: str) -> List[str]:\n raise NotImplementedError\n\n @staticmethod\n def check_placeholder_pattern(example):\n placeholder_patterns = [\n \"This is a placeholder ####\",\n \"No chain-of-thought provided\"\n ]\n for patt in placeholder_patterns:\n if patt in example:\n return True\n return False\n\n @staticmethod\n def convert_examples_to_features(\n examples: List[InputExample],\n label_list: List[str],\n max_seq_length: int,\n tokenizer: PreTrainedTokenizer,\n cls_token_at_end=False,\n cls_token=\"[CLS]\",\n cls_token_segment_id=1,\n sep_token=\"[SEP]\",\n sep_token_extra=False,\n pad_on_left=False,\n pad_token=0,\n pad_token_segment_id=0,\n pad_token_label_id=-100,\n sequence_a_segment_id=0,\n mask_padding_with_zero=True,\n mode: Split = Split.dev\n ) -> List[InputFeatures]:\n \"\"\"Loads a data file into a list of `InputFeatures`\n `cls_token_at_end` define the location of the CLS token:\n - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP]\n - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS]\n `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet)\n \"\"\"\n # TODO clean up all this to leverage built-in features of tokenizers\n\n label_map = {label: i for i, label in enumerate(label_list)}\n label_map[\"O\"] = pad_token_label_id\n\n features = []\n for (ex_index, example) in enumerate(examples):\n if ex_index % 10_000 == 0:\n logger.info(\"Writing example %d of %d\", ex_index, len(examples))\n\n # skip the placeholder examples\n example_str = \" \".join(example.words)\n if ex_index == 0:\n print(example_str)\n if mode == Split.train and TokenClassificationTask.check_placeholder_pattern(example_str):\n continue\n\n tokens = []\n label_ids = []\n for word, label in zip(example.words, example.labels):\n word_tokens = tokenizer.tokenize(word)\n\n # bert-base-multilingual-cased sometimes output \"nothing ([]) when calling tokenize with just a space.\n if len(word_tokens) > 0:\n tokens.extend(word_tokens)\n # Use the real label id for the first token of the word, and padding ids for the remaining tokens\n label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1))\n\n # Account for [CLS] and [SEP] with \"- 2\" and with \"- 3\" for RoBERTa.\n special_tokens_count = tokenizer.num_special_tokens_to_add()\n if len(tokens) > max_seq_length - special_tokens_count:\n tokens = tokens[: (max_seq_length - special_tokens_count)]\n label_ids = label_ids[: (max_seq_length - special_tokens_count)]\n\n # The convention in BERT is:\n # (a) For sequence pairs:\n # tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]\n # type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1\n # (b) For single sequences:\n # tokens: [CLS] the dog is hairy . [SEP]\n # type_ids: 0 0 0 0 0 0 0\n #\n # Where \"type_ids\" are used to indicate whether this is the first\n # sequence or the second sequence. The embedding vectors for `type=0` and\n # `type=1` were learned during pre-training and are added to the wordpiece\n # embedding vector (and position vector). This is not *strictly* necessary\n # since the [SEP] token unambiguously separates the sequences, but it makes\n # it easier for the model to learn the concept of sequences.\n #\n # For classification tasks, the first vector (corresponding to [CLS]) is\n # used as as the \"sentence vector\". Note that this only makes sense because\n # the entire model is fine-tuned.\n tokens += [sep_token]\n label_ids += [pad_token_label_id]\n if sep_token_extra:\n # roberta uses an extra separator b/w pairs of sentences\n tokens += [sep_token]\n label_ids += [pad_token_label_id]\n segment_ids = [sequence_a_segment_id] * len(tokens)\n\n if cls_token_at_end:\n tokens += [cls_token]\n label_ids += [pad_token_label_id]\n segment_ids += [cls_token_segment_id]\n else:\n tokens = [cls_token] + tokens\n label_ids = [pad_token_label_id] + label_ids\n segment_ids = [cls_token_segment_id] + segment_ids\n\n input_ids = tokenizer.convert_tokens_to_ids(tokens)\n\n # The mask has 1 for real tokens and 0 for padding tokens. Only real\n # tokens are attended to.\n input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)\n\n # Zero-pad up to the sequence length.\n padding_length = max_seq_length - len(input_ids)\n if pad_on_left:\n input_ids = ([pad_token] * padding_length) + input_ids\n input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask\n segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids\n label_ids = ([pad_token_label_id] * padding_length) + label_ids\n else:\n input_ids += [pad_token] * padding_length\n input_mask += [0 if mask_padding_with_zero else 1] * padding_length\n segment_ids += [pad_token_segment_id] * padding_length\n label_ids += [pad_token_label_id] * padding_length\n\n assert len(input_ids) == max_seq_length\n assert len(input_mask) == max_seq_length\n assert len(segment_ids) == max_seq_length\n assert len(label_ids) == max_seq_length\n\n if ex_index < 5:\n logger.info(\"*** Example ***\")\n logger.info(\"guid: %s\", example.guid)\n logger.info(\"tokens: %s\", \" \".join([str(x) for x in tokens]))\n logger.info(\"input_ids: %s\", \" \".join([str(x) for x in input_ids]))\n logger.info(\"input_mask: %s\", \" \".join([str(x) for x in input_mask]))\n logger.info(\"segment_ids: %s\", \" \".join([str(x) for x in segment_ids]))\n logger.info(\"label_ids: %s\", \" \".join([str(x) for x in label_ids]))\n\n if \"token_type_ids\" not in tokenizer.model_input_names:\n segment_ids = None\n\n features.append(\n InputFeatures(\n input_ids=input_ids, attention_mask=input_mask, token_type_ids=segment_ids, label_ids=label_ids\n )\n )\n return features\n\n\nif is_torch_available():\n import torch\n from torch import nn\n from torch.utils.data import Dataset\n\n class TokenClassificationDataset(Dataset):\n \"\"\"\n This will be superseded by a framework-agnostic approach\n soon.\n \"\"\"\n\n features: List[InputFeatures]\n pad_token_label_id: int = nn.CrossEntropyLoss().ignore_index\n # Use cross entropy ignore_index as padding label id so that only\n # real label ids contribute to the loss later.\n\n def __init__(\n self,","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner.TokenClassificationDataset","uri":"program://CodeT/class/DIVERSE.code.src.utils_ner.TokenClassificationDataset#L232-L297","kind":"class","name":"TokenClassificationDataset","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":232,"end_line":297,"context_start_line":212,"context_end_line":317,"code":" logger.info(\"input_mask: %s\", \" \".join([str(x) for x in input_mask]))\n logger.info(\"segment_ids: %s\", \" \".join([str(x) for x in segment_ids]))\n logger.info(\"label_ids: %s\", \" \".join([str(x) for x in label_ids]))\n\n if \"token_type_ids\" not in tokenizer.model_input_names:\n segment_ids = None\n\n features.append(\n InputFeatures(\n input_ids=input_ids, attention_mask=input_mask, token_type_ids=segment_ids, label_ids=label_ids\n )\n )\n return features\n\n\nif is_torch_available():\n import torch\n from torch import nn\n from torch.utils.data import Dataset\n\n class TokenClassificationDataset(Dataset):\n \"\"\"\n This will be superseded by a framework-agnostic approach\n soon.\n \"\"\"\n\n features: List[InputFeatures]\n pad_token_label_id: int = nn.CrossEntropyLoss().ignore_index\n # Use cross entropy ignore_index as padding label id so that only\n # real label ids contribute to the loss later.\n\n def __init__(\n self,\n token_classification_task: TokenClassificationTask,\n data_dir: str,\n tokenizer: PreTrainedTokenizer,\n labels: List[str],\n model_type: str,\n max_seq_length: Optional[int] = None,\n overwrite_cache=False,\n mode: Split = Split.train,\n ):\n # Load data features from cache or dataset file\n cached_features_file = os.path.join(\n data_dir,\n \"cached_{}_{}_{}\".format(mode.value, tokenizer.__class__.__name__, str(max_seq_length)),\n )\n\n # Make sure only the first process in distributed training processes the dataset,\n # and the others will use the cache.\n lock_path = cached_features_file + \".lock\"\n with FileLock(lock_path):\n\n if os.path.exists(cached_features_file) and not overwrite_cache:\n logger.error(f\"Loading features from cached file {cached_features_file}\")\n self.features = torch.load(cached_features_file)\n else:\n logger.error(f\"Creating features from dataset file at {data_dir}\")\n examples = token_classification_task.read_examples_from_file(data_dir, mode)\n # TODO clean up all this to leverage built-in features of tokenizers\n self.features = token_classification_task.convert_examples_to_features(\n examples,\n labels,\n max_seq_length,\n tokenizer,\n cls_token_at_end=bool(model_type in [\"xlnet\"]),\n # xlnet has a cls token at the end\n cls_token=tokenizer.cls_token,\n cls_token_segment_id=2 if model_type in [\"xlnet\"] else 0,\n sep_token=tokenizer.sep_token,\n sep_token_extra=False,\n # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805\n pad_on_left=bool(tokenizer.padding_side == \"left\"),\n pad_token=tokenizer.pad_token_id,\n pad_token_segment_id=tokenizer.pad_token_type_id,\n pad_token_label_id=self.pad_token_label_id,\n mode = mode\n )\n logger.error(f\"Saving features into cached file {cached_features_file}\")\n torch.save(self.features, cached_features_file)\n\n def __len__(self):\n return len(self.features)\n\n def __getitem__(self, i) -> InputFeatures:\n return self.features[i]\n\n\nif is_tf_available():\n import tensorflow as tf\n\n class TFTokenClassificationDataset:\n \"\"\"\n This will be superseded by a framework-agnostic approach\n soon.\n \"\"\"\n\n features: List[InputFeatures]\n pad_token_label_id: int = -100\n # Use cross entropy ignore_index as padding label id so that only\n # real label ids contribute to the loss later.\n\n def __init__(\n self,\n token_classification_task: TokenClassificationTask,\n data_dir: str,","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner.TFTokenClassificationDataset","uri":"program://CodeT/class/DIVERSE.code.src.utils_ner.TFTokenClassificationDataset#L303-L395","kind":"class","name":"TFTokenClassificationDataset","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":303,"end_line":395,"context_start_line":283,"context_end_line":395,"code":" # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805\n pad_on_left=bool(tokenizer.padding_side == \"left\"),\n pad_token=tokenizer.pad_token_id,\n pad_token_segment_id=tokenizer.pad_token_type_id,\n pad_token_label_id=self.pad_token_label_id,\n mode = mode\n )\n logger.error(f\"Saving features into cached file {cached_features_file}\")\n torch.save(self.features, cached_features_file)\n\n def __len__(self):\n return len(self.features)\n\n def __getitem__(self, i) -> InputFeatures:\n return self.features[i]\n\n\nif is_tf_available():\n import tensorflow as tf\n\n class TFTokenClassificationDataset:\n \"\"\"\n This will be superseded by a framework-agnostic approach\n soon.\n \"\"\"\n\n features: List[InputFeatures]\n pad_token_label_id: int = -100\n # Use cross entropy ignore_index as padding label id so that only\n # real label ids contribute to the loss later.\n\n def __init__(\n self,\n token_classification_task: TokenClassificationTask,\n data_dir: str,\n tokenizer: PreTrainedTokenizer,\n labels: List[str],\n model_type: str,\n max_seq_length: Optional[int] = None,\n overwrite_cache=False,\n mode: Split = Split.train,\n ):\n examples = token_classification_task.read_examples_from_file(data_dir, mode)\n # TODO clean up all this to leverage built-in features of tokenizers\n self.features = token_classification_task.convert_examples_to_features(\n examples,\n labels,\n max_seq_length,\n tokenizer,\n cls_token_at_end=bool(model_type in [\"xlnet\"]),\n # xlnet has a cls token at the end\n cls_token=tokenizer.cls_token,\n cls_token_segment_id=2 if model_type in [\"xlnet\"] else 0,\n sep_token=tokenizer.sep_token,\n sep_token_extra=False,\n # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805\n pad_on_left=bool(tokenizer.padding_side == \"left\"),\n pad_token=tokenizer.pad_token_id,\n pad_token_segment_id=tokenizer.pad_token_type_id,\n pad_token_label_id=self.pad_token_label_id,\n mode = mode\n )\n\n def gen():\n for ex in self.features:\n if ex.token_type_ids is None:\n yield (\n {\"input_ids\": ex.input_ids, \"attention_mask\": ex.attention_mask},\n ex.label_ids,\n )\n else:\n yield (\n {\n \"input_ids\": ex.input_ids,\n \"attention_mask\": ex.attention_mask,\n \"token_type_ids\": ex.token_type_ids,\n },\n ex.label_ids,\n )\n\n if \"token_type_ids\" not in tokenizer.model_input_names:\n self.dataset = tf.data.Dataset.from_generator(\n gen,\n ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32}, tf.int64),\n (\n {\"input_ids\": tf.TensorShape([None]), \"attention_mask\": tf.TensorShape([None])},\n tf.TensorShape([None]),\n ),\n )\n else:\n self.dataset = tf.data.Dataset.from_generator(\n gen,\n ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32, \"token_type_ids\": tf.int32}, tf.int64),\n (\n {\n \"input_ids\": tf.TensorShape([None]),\n \"attention_mask\": tf.TensorShape([None]),\n \"token_type_ids\": tf.TensorShape([None]),\n },\n tf.TensorShape([None]),\n ),\n )\n\n def get_dataset(self):\n self.dataset = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))\n\n return self.dataset\n\n def __len__(self):\n return len(self.features)\n\n def __getitem__(self, i) -> InputFeatures:\n return self.features[i]","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner.__init__","uri":"program://CodeT/function/DIVERSE.code.src.utils_ner.__init__#L314-L384","kind":"function","name":"__init__","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":314,"end_line":384,"context_start_line":294,"context_end_line":395,"code":" return len(self.features)\n\n def __getitem__(self, i) -> InputFeatures:\n return self.features[i]\n\n\nif is_tf_available():\n import tensorflow as tf\n\n class TFTokenClassificationDataset:\n \"\"\"\n This will be superseded by a framework-agnostic approach\n soon.\n \"\"\"\n\n features: List[InputFeatures]\n pad_token_label_id: int = -100\n # Use cross entropy ignore_index as padding label id so that only\n # real label ids contribute to the loss later.\n\n def __init__(\n self,\n token_classification_task: TokenClassificationTask,\n data_dir: str,\n tokenizer: PreTrainedTokenizer,\n labels: List[str],\n model_type: str,\n max_seq_length: Optional[int] = None,\n overwrite_cache=False,\n mode: Split = Split.train,\n ):\n examples = token_classification_task.read_examples_from_file(data_dir, mode)\n # TODO clean up all this to leverage built-in features of tokenizers\n self.features = token_classification_task.convert_examples_to_features(\n examples,\n labels,\n max_seq_length,\n tokenizer,\n cls_token_at_end=bool(model_type in [\"xlnet\"]),\n # xlnet has a cls token at the end\n cls_token=tokenizer.cls_token,\n cls_token_segment_id=2 if model_type in [\"xlnet\"] else 0,\n sep_token=tokenizer.sep_token,\n sep_token_extra=False,\n # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805\n pad_on_left=bool(tokenizer.padding_side == \"left\"),\n pad_token=tokenizer.pad_token_id,\n pad_token_segment_id=tokenizer.pad_token_type_id,\n pad_token_label_id=self.pad_token_label_id,\n mode = mode\n )\n\n def gen():\n for ex in self.features:\n if ex.token_type_ids is None:\n yield (\n {\"input_ids\": ex.input_ids, \"attention_mask\": ex.attention_mask},\n ex.label_ids,\n )\n else:\n yield (\n {\n \"input_ids\": ex.input_ids,\n \"attention_mask\": ex.attention_mask,\n \"token_type_ids\": ex.token_type_ids,\n },\n ex.label_ids,\n )\n\n if \"token_type_ids\" not in tokenizer.model_input_names:\n self.dataset = tf.data.Dataset.from_generator(\n gen,\n ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32}, tf.int64),\n (\n {\"input_ids\": tf.TensorShape([None]), \"attention_mask\": tf.TensorShape([None])},\n tf.TensorShape([None]),\n ),\n )\n else:\n self.dataset = tf.data.Dataset.from_generator(\n gen,\n ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32, \"token_type_ids\": tf.int32}, tf.int64),\n (\n {\n \"input_ids\": tf.TensorShape([None]),\n \"attention_mask\": tf.TensorShape([None]),\n \"token_type_ids\": tf.TensorShape([None]),\n },\n tf.TensorShape([None]),\n ),\n )\n\n def get_dataset(self):\n self.dataset = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))\n\n return self.dataset\n\n def __len__(self):\n return len(self.features)\n\n def __getitem__(self, i) -> InputFeatures:\n return self.features[i]","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner.__len__","uri":"program://CodeT/function/DIVERSE.code.src.utils_ner.__len__#L391-L392","kind":"function","name":"__len__","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":391,"end_line":392,"context_start_line":371,"context_end_line":395,"code":" )\n else:\n self.dataset = tf.data.Dataset.from_generator(\n gen,\n ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32, \"token_type_ids\": tf.int32}, tf.int64),\n (\n {\n \"input_ids\": tf.TensorShape([None]),\n \"attention_mask\": tf.TensorShape([None]),\n \"token_type_ids\": tf.TensorShape([None]),\n },\n tf.TensorShape([None]),\n ),\n )\n\n def get_dataset(self):\n self.dataset = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))\n\n return self.dataset\n\n def __len__(self):\n return len(self.features)\n\n def __getitem__(self, i) -> InputFeatures:\n return self.features[i]","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner.__getitem__","uri":"program://CodeT/function/DIVERSE.code.src.utils_ner.__getitem__#L394-L395","kind":"function","name":"__getitem__","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":394,"end_line":395,"context_start_line":374,"context_end_line":395,"code":" gen,\n ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32, \"token_type_ids\": tf.int32}, tf.int64),\n (\n {\n \"input_ids\": tf.TensorShape([None]),\n \"attention_mask\": tf.TensorShape([None]),\n \"token_type_ids\": tf.TensorShape([None]),\n },\n tf.TensorShape([None]),\n ),\n )\n\n def get_dataset(self):\n self.dataset = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))\n\n return self.dataset\n\n def __len__(self):\n return len(self.features)\n\n def __getitem__(self, i) -> InputFeatures:\n return self.features[i]","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner.get_dataset","uri":"program://CodeT/function/DIVERSE.code.src.utils_ner.get_dataset#L386-L389","kind":"function","name":"get_dataset","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":386,"end_line":389,"context_start_line":366,"context_end_line":395,"code":" ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32}, tf.int64),\n (\n {\"input_ids\": tf.TensorShape([None]), \"attention_mask\": tf.TensorShape([None])},\n tf.TensorShape([None]),\n ),\n )\n else:\n self.dataset = tf.data.Dataset.from_generator(\n gen,\n ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32, \"token_type_ids\": tf.int32}, tf.int64),\n (\n {\n \"input_ids\": tf.TensorShape([None]),\n \"attention_mask\": tf.TensorShape([None]),\n \"token_type_ids\": tf.TensorShape([None]),\n },\n tf.TensorShape([None]),\n ),\n )\n\n def get_dataset(self):\n self.dataset = self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))\n\n return self.dataset\n\n def __len__(self):\n return len(self.features)\n\n def __getitem__(self, i) -> InputFeatures:\n return self.features[i]","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.utils_ner.gen","uri":"program://CodeT/function/DIVERSE.code.src.utils_ner.gen#L346-L361","kind":"function","name":"gen","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":346,"end_line":361,"context_start_line":326,"context_end_line":381,"code":" # TODO clean up all this to leverage built-in features of tokenizers\n self.features = token_classification_task.convert_examples_to_features(\n examples,\n labels,\n max_seq_length,\n tokenizer,\n cls_token_at_end=bool(model_type in [\"xlnet\"]),\n # xlnet has a cls token at the end\n cls_token=tokenizer.cls_token,\n cls_token_segment_id=2 if model_type in [\"xlnet\"] else 0,\n sep_token=tokenizer.sep_token,\n sep_token_extra=False,\n # roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805\n pad_on_left=bool(tokenizer.padding_side == \"left\"),\n pad_token=tokenizer.pad_token_id,\n pad_token_segment_id=tokenizer.pad_token_type_id,\n pad_token_label_id=self.pad_token_label_id,\n mode = mode\n )\n\n def gen():\n for ex in self.features:\n if ex.token_type_ids is None:\n yield (\n {\"input_ids\": ex.input_ids, \"attention_mask\": ex.attention_mask},\n ex.label_ids,\n )\n else:\n yield (\n {\n \"input_ids\": ex.input_ids,\n \"attention_mask\": ex.attention_mask,\n \"token_type_ids\": ex.token_type_ids,\n },\n ex.label_ids,\n )\n\n if \"token_type_ids\" not in tokenizer.model_input_names:\n self.dataset = tf.data.Dataset.from_generator(\n gen,\n ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32}, tf.int64),\n (\n {\"input_ids\": tf.TensorShape([None]), \"attention_mask\": tf.TensorShape([None])},\n tf.TensorShape([None]),\n ),\n )\n else:\n self.dataset = tf.data.Dataset.from_generator(\n gen,\n ({\"input_ids\": tf.int32, \"attention_mask\": tf.int32, \"token_type_ids\": tf.int32}, tf.int64),\n (\n {\n \"input_ids\": tf.TensorShape([None]),\n \"attention_mask\": tf.TensorShape([None]),\n \"token_type_ids\": tf.TensorShape([None]),\n },","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.tasks","uri":"program://CodeT/module/DIVERSE.code.src.tasks#L1-L163","kind":"module","name":"DIVERSE.code.src.tasks","path":"DIVERSE/code/src/tasks.py","language":"python","start_line":1,"end_line":163,"context_start_line":1,"context_end_line":163,"code":"import logging\nimport os\nfrom typing import List, TextIO, Union\n\nfrom conllu import parse_incr\n\nfrom utils_ner import InputExample, Split, TokenClassificationTask\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass NER(TokenClassificationTask):\n def __init__(self, label_idx=-1):\n # in NER datasets, the last column is usually reserved for NER label\n self.label_idx = label_idx\n\n def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]:\n if isinstance(mode, Split):\n mode = mode.value\n file_path = os.path.join(data_dir, f\"{mode}.txt\")\n guid_index = 1\n examples = []\n with open(file_path, encoding=\"utf-8\") as f:\n words = []\n labels = []\n for line in f:\n if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\n if words:\n examples.append(InputExample(guid=f\"{mode}-{guid_index}\", words=words, labels=labels))\n guid_index += 1\n words = []\n labels = []\n else:\n splits = line.split(\" \")\n words.append(splits[0])\n if len(splits) > 1:\n labels.append(splits[self.label_idx].replace(\"\\n\", \"\"))\n else:\n # Examples could have no label for mode = \"test\"\n labels.append(\"O\")\n if words:\n examples.append(InputExample(guid=f\"{mode}-{guid_index}\", words=words, labels=labels))\n return examples\n\n def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: List):\n example_id = 0\n for line in test_input_reader:\n if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\n writer.write(line)\n if not preds_list[example_id]:\n example_id += 1\n elif preds_list[example_id]:\n output_line = line.split()[0] + \" \" + preds_list[example_id].pop(0) + \"\\n\"\n writer.write(output_line)\n else:\n logger.warning(\"Maximum sequence length exceeded: No prediction for '%s'.\", line.split()[0])\n\n def get_labels(self, path: str) -> List[str]:\n if path:\n with open(path, \"r\") as f:\n labels = f.read().splitlines()\n if \"O\" not in labels:\n labels = [\"O\"] + labels\n return labels\n else:\n return [\"O\", \"B-MISC\", \"I-MISC\", \"B-PER\", \"I-PER\", \"B-ORG\", \"I-ORG\", \"B-LOC\", \"I-LOC\"]\n\n\nclass Chunk(NER):\n def __init__(self):\n # in CONLL2003 dataset chunk column is second-to-last\n super().__init__(label_idx=-2)\n\n def get_labels(self, path: str) -> List[str]:\n if path:\n with open(path, \"r\") as f:\n labels = f.read().splitlines()\n if \"O\" not in labels:\n labels = [\"O\"] + labels\n return labels\n else:\n return [\n \"O\",\n \"B-ADVP\",\n \"B-INTJ\",\n \"B-LST\",\n \"B-PRT\",\n \"B-NP\",\n \"B-SBAR\",\n \"B-VP\",\n \"B-ADJP\",\n \"B-CONJP\",\n \"B-PP\",\n \"I-ADVP\",\n \"I-INTJ\",\n \"I-LST\",\n \"I-PRT\",\n \"I-NP\",\n \"I-SBAR\",\n \"I-VP\",\n \"I-ADJP\",\n \"I-CONJP\",\n \"I-PP\",\n ]\n\n\nclass POS(TokenClassificationTask):\n def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]:\n if isinstance(mode, Split):\n mode = mode.value\n file_path = os.path.join(data_dir, f\"{mode}.txt\")\n guid_index = 1\n examples = []\n\n with open(file_path, encoding=\"utf-8\") as f:\n for sentence in parse_incr(f):\n words = []\n labels = []\n for token in sentence:\n words.append(token[\"form\"])\n labels.append(token[\"upos\"])\n assert len(words) == len(labels)\n if words:\n examples.append(InputExample(guid=f\"{mode}-{guid_index}\", words=words, labels=labels))\n guid_index += 1\n return examples\n\n def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: List):\n example_id = 0\n for sentence in parse_incr(test_input_reader):\n s_p = preds_list[example_id]\n out = \"\"\n for token in sentence:\n out += f'{token[\"form\"]} ({token[\"upos\"]}|{s_p.pop(0)}) '\n out += \"\\n\"\n writer.write(out)\n example_id += 1\n\n def get_labels(self, path: str) -> List[str]:\n if path:\n with open(path, \"r\") as f:\n return f.read().splitlines()\n else:\n return [\n \"ADJ\",\n \"ADP\",\n \"ADV\",\n \"AUX\",\n \"CCONJ\",\n \"DET\",\n \"INTJ\",\n \"NOUN\",\n \"NUM\",\n \"PART\",\n \"PRON\",\n \"PROPN\",\n \"PUNCT\",\n \"SCONJ\",\n \"SYM\",\n \"VERB\",\n \"X\",\n ]","source_hash":"6c47ec218bcb6c0bc4fe1fc726384a91ab255e0f750aa4e0b1ee2dee7c549579","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.tasks.NER","uri":"program://CodeT/class/DIVERSE.code.src.tasks.NER#L13-L67","kind":"class","name":"NER","path":"DIVERSE/code/src/tasks.py","language":"python","start_line":13,"end_line":67,"context_start_line":1,"context_end_line":87,"code":"import logging\nimport os\nfrom typing import List, TextIO, Union\n\nfrom conllu import parse_incr\n\nfrom utils_ner import InputExample, Split, TokenClassificationTask\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass NER(TokenClassificationTask):\n def __init__(self, label_idx=-1):\n # in NER datasets, the last column is usually reserved for NER label\n self.label_idx = label_idx\n\n def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]:\n if isinstance(mode, Split):\n mode = mode.value\n file_path = os.path.join(data_dir, f\"{mode}.txt\")\n guid_index = 1\n examples = []\n with open(file_path, encoding=\"utf-8\") as f:\n words = []\n labels = []\n for line in f:\n if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\n if words:\n examples.append(InputExample(guid=f\"{mode}-{guid_index}\", words=words, labels=labels))\n guid_index += 1\n words = []\n labels = []\n else:\n splits = line.split(\" \")\n words.append(splits[0])\n if len(splits) > 1:\n labels.append(splits[self.label_idx].replace(\"\\n\", \"\"))\n else:\n # Examples could have no label for mode = \"test\"\n labels.append(\"O\")\n if words:\n examples.append(InputExample(guid=f\"{mode}-{guid_index}\", words=words, labels=labels))\n return examples\n\n def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: List):\n example_id = 0\n for line in test_input_reader:\n if line.startswith(\"-DOCSTART-\") or line == \"\" or line == \"\\n\":\n writer.write(line)\n if not preds_list[example_id]:\n example_id += 1\n elif preds_list[example_id]:\n output_line = line.split()[0] + \" \" + preds_list[example_id].pop(0) + \"\\n\"\n writer.write(output_line)\n else:\n logger.warning(\"Maximum sequence length exceeded: No prediction for '%s'.\", line.split()[0])\n\n def get_labels(self, path: str) -> List[str]:\n if path:\n with open(path, \"r\") as f:\n labels = f.read().splitlines()\n if \"O\" not in labels:\n labels = [\"O\"] + labels\n return labels\n else:\n return [\"O\", \"B-MISC\", \"I-MISC\", \"B-PER\", \"I-PER\", \"B-ORG\", \"I-ORG\", \"B-LOC\", \"I-LOC\"]\n\n\nclass Chunk(NER):\n def __init__(self):\n # in CONLL2003 dataset chunk column is second-to-last\n super().__init__(label_idx=-2)\n\n def get_labels(self, path: str) -> List[str]:\n if path:\n with open(path, \"r\") as f:\n labels = f.read().splitlines()\n if \"O\" not in labels:\n labels = [\"O\"] + labels\n return labels\n else:\n return [\n \"O\",\n \"B-ADVP\",\n \"B-INTJ\",\n \"B-LST\",","source_hash":"6c47ec218bcb6c0bc4fe1fc726384a91ab255e0f750aa4e0b1ee2dee7c549579","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.tasks.Chunk","uri":"program://CodeT/class/DIVERSE.code.src.tasks.Chunk#L70-L105","kind":"class","name":"Chunk","path":"DIVERSE/code/src/tasks.py","language":"python","start_line":70,"end_line":105,"context_start_line":50,"context_end_line":125,"code":" writer.write(line)\n if not preds_list[example_id]:\n example_id += 1\n elif preds_list[example_id]:\n output_line = line.split()[0] + \" \" + preds_list[example_id].pop(0) + \"\\n\"\n writer.write(output_line)\n else:\n logger.warning(\"Maximum sequence length exceeded: No prediction for '%s'.\", line.split()[0])\n\n def get_labels(self, path: str) -> List[str]:\n if path:\n with open(path, \"r\") as f:\n labels = f.read().splitlines()\n if \"O\" not in labels:\n labels = [\"O\"] + labels\n return labels\n else:\n return [\"O\", \"B-MISC\", \"I-MISC\", \"B-PER\", \"I-PER\", \"B-ORG\", \"I-ORG\", \"B-LOC\", \"I-LOC\"]\n\n\nclass Chunk(NER):\n def __init__(self):\n # in CONLL2003 dataset chunk column is second-to-last\n super().__init__(label_idx=-2)\n\n def get_labels(self, path: str) -> List[str]:\n if path:\n with open(path, \"r\") as f:\n labels = f.read().splitlines()\n if \"O\" not in labels:\n labels = [\"O\"] + labels\n return labels\n else:\n return [\n \"O\",\n \"B-ADVP\",\n \"B-INTJ\",\n \"B-LST\",\n \"B-PRT\",\n \"B-NP\",\n \"B-SBAR\",\n \"B-VP\",\n \"B-ADJP\",\n \"B-CONJP\",\n \"B-PP\",\n \"I-ADVP\",\n \"I-INTJ\",\n \"I-LST\",\n \"I-PRT\",\n \"I-NP\",\n \"I-SBAR\",\n \"I-VP\",\n \"I-ADJP\",\n \"I-CONJP\",\n \"I-PP\",\n ]\n\n\nclass POS(TokenClassificationTask):\n def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]:\n if isinstance(mode, Split):\n mode = mode.value\n file_path = os.path.join(data_dir, f\"{mode}.txt\")\n guid_index = 1\n examples = []\n\n with open(file_path, encoding=\"utf-8\") as f:\n for sentence in parse_incr(f):\n words = []\n labels = []\n for token in sentence:\n words.append(token[\"form\"])\n labels.append(token[\"upos\"])\n assert len(words) == len(labels)\n if words:\n examples.append(InputExample(guid=f\"{mode}-{guid_index}\", words=words, labels=labels))","source_hash":"6c47ec218bcb6c0bc4fe1fc726384a91ab255e0f750aa4e0b1ee2dee7c549579","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.tasks.POS","uri":"program://CodeT/class/DIVERSE.code.src.tasks.POS#L108-L163","kind":"class","name":"POS","path":"DIVERSE/code/src/tasks.py","language":"python","start_line":108,"end_line":163,"context_start_line":88,"context_end_line":163,"code":" \"B-PRT\",\n \"B-NP\",\n \"B-SBAR\",\n \"B-VP\",\n \"B-ADJP\",\n \"B-CONJP\",\n \"B-PP\",\n \"I-ADVP\",\n \"I-INTJ\",\n \"I-LST\",\n \"I-PRT\",\n \"I-NP\",\n \"I-SBAR\",\n \"I-VP\",\n \"I-ADJP\",\n \"I-CONJP\",\n \"I-PP\",\n ]\n\n\nclass POS(TokenClassificationTask):\n def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]:\n if isinstance(mode, Split):\n mode = mode.value\n file_path = os.path.join(data_dir, f\"{mode}.txt\")\n guid_index = 1\n examples = []\n\n with open(file_path, encoding=\"utf-8\") as f:\n for sentence in parse_incr(f):\n words = []\n labels = []\n for token in sentence:\n words.append(token[\"form\"])\n labels.append(token[\"upos\"])\n assert len(words) == len(labels)\n if words:\n examples.append(InputExample(guid=f\"{mode}-{guid_index}\", words=words, labels=labels))\n guid_index += 1\n return examples\n\n def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: List):\n example_id = 0\n for sentence in parse_incr(test_input_reader):\n s_p = preds_list[example_id]\n out = \"\"\n for token in sentence:\n out += f'{token[\"form\"]} ({token[\"upos\"]}|{s_p.pop(0)}) '\n out += \"\\n\"\n writer.write(out)\n example_id += 1\n\n def get_labels(self, path: str) -> List[str]:\n if path:\n with open(path, \"r\") as f:\n return f.read().splitlines()\n else:\n return [\n \"ADJ\",\n \"ADP\",\n \"ADV\",\n \"AUX\",\n \"CCONJ\",\n \"DET\",\n \"INTJ\",\n \"NOUN\",\n \"NUM\",\n \"PART\",\n \"PRON\",\n \"PROPN\",\n \"PUNCT\",\n \"SCONJ\",\n \"SYM\",\n \"VERB\",\n \"X\",\n ]","source_hash":"6c47ec218bcb6c0bc4fe1fc726384a91ab255e0f750aa4e0b1ee2dee7c549579","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.tasks.__init__","uri":"program://CodeT/function/DIVERSE.code.src.tasks.__init__#L71-L73","kind":"function","name":"__init__","path":"DIVERSE/code/src/tasks.py","language":"python","start_line":71,"end_line":73,"context_start_line":51,"context_end_line":93,"code":" if not preds_list[example_id]:\n example_id += 1\n elif preds_list[example_id]:\n output_line = line.split()[0] + \" \" + preds_list[example_id].pop(0) + \"\\n\"\n writer.write(output_line)\n else:\n logger.warning(\"Maximum sequence length exceeded: No prediction for '%s'.\", line.split()[0])\n\n def get_labels(self, path: str) -> List[str]:\n if path:\n with open(path, \"r\") as f:\n labels = f.read().splitlines()\n if \"O\" not in labels:\n labels = [\"O\"] + labels\n return labels\n else:\n return [\"O\", \"B-MISC\", \"I-MISC\", \"B-PER\", \"I-PER\", \"B-ORG\", \"I-ORG\", \"B-LOC\", \"I-LOC\"]\n\n\nclass Chunk(NER):\n def __init__(self):\n # in CONLL2003 dataset chunk column is second-to-last\n super().__init__(label_idx=-2)\n\n def get_labels(self, path: str) -> List[str]:\n if path:\n with open(path, \"r\") as f:\n labels = f.read().splitlines()\n if \"O\" not in labels:\n labels = [\"O\"] + labels\n return labels\n else:\n return [\n \"O\",\n \"B-ADVP\",\n \"B-INTJ\",\n \"B-LST\",\n \"B-PRT\",\n \"B-NP\",\n \"B-SBAR\",\n \"B-VP\",\n \"B-ADJP\",\n \"B-CONJP\",","source_hash":"6c47ec218bcb6c0bc4fe1fc726384a91ab255e0f750aa4e0b1ee2dee7c549579","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.tasks.read_examples_from_file","uri":"program://CodeT/function/DIVERSE.code.src.tasks.read_examples_from_file#L109-L127","kind":"function","name":"read_examples_from_file","path":"DIVERSE/code/src/tasks.py","language":"python","start_line":109,"end_line":127,"context_start_line":89,"context_end_line":147,"code":" \"B-NP\",\n \"B-SBAR\",\n \"B-VP\",\n \"B-ADJP\",\n \"B-CONJP\",\n \"B-PP\",\n \"I-ADVP\",\n \"I-INTJ\",\n \"I-LST\",\n \"I-PRT\",\n \"I-NP\",\n \"I-SBAR\",\n \"I-VP\",\n \"I-ADJP\",\n \"I-CONJP\",\n \"I-PP\",\n ]\n\n\nclass POS(TokenClassificationTask):\n def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]:\n if isinstance(mode, Split):\n mode = mode.value\n file_path = os.path.join(data_dir, f\"{mode}.txt\")\n guid_index = 1\n examples = []\n\n with open(file_path, encoding=\"utf-8\") as f:\n for sentence in parse_incr(f):\n words = []\n labels = []\n for token in sentence:\n words.append(token[\"form\"])\n labels.append(token[\"upos\"])\n assert len(words) == len(labels)\n if words:\n examples.append(InputExample(guid=f\"{mode}-{guid_index}\", words=words, labels=labels))\n guid_index += 1\n return examples\n\n def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: List):\n example_id = 0\n for sentence in parse_incr(test_input_reader):\n s_p = preds_list[example_id]\n out = \"\"\n for token in sentence:\n out += f'{token[\"form\"]} ({token[\"upos\"]}|{s_p.pop(0)}) '\n out += \"\\n\"\n writer.write(out)\n example_id += 1\n\n def get_labels(self, path: str) -> List[str]:\n if path:\n with open(path, \"r\") as f:\n return f.read().splitlines()\n else:\n return [\n \"ADJ\",\n \"ADP\",","source_hash":"6c47ec218bcb6c0bc4fe1fc726384a91ab255e0f750aa4e0b1ee2dee7c549579","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.tasks.write_predictions_to_file","uri":"program://CodeT/function/DIVERSE.code.src.tasks.write_predictions_to_file#L129-L138","kind":"function","name":"write_predictions_to_file","path":"DIVERSE/code/src/tasks.py","language":"python","start_line":129,"end_line":138,"context_start_line":109,"context_end_line":158,"code":" def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]:\n if isinstance(mode, Split):\n mode = mode.value\n file_path = os.path.join(data_dir, f\"{mode}.txt\")\n guid_index = 1\n examples = []\n\n with open(file_path, encoding=\"utf-8\") as f:\n for sentence in parse_incr(f):\n words = []\n labels = []\n for token in sentence:\n words.append(token[\"form\"])\n labels.append(token[\"upos\"])\n assert len(words) == len(labels)\n if words:\n examples.append(InputExample(guid=f\"{mode}-{guid_index}\", words=words, labels=labels))\n guid_index += 1\n return examples\n\n def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: List):\n example_id = 0\n for sentence in parse_incr(test_input_reader):\n s_p = preds_list[example_id]\n out = \"\"\n for token in sentence:\n out += f'{token[\"form\"]} ({token[\"upos\"]}|{s_p.pop(0)}) '\n out += \"\\n\"\n writer.write(out)\n example_id += 1\n\n def get_labels(self, path: str) -> List[str]:\n if path:\n with open(path, \"r\") as f:\n return f.read().splitlines()\n else:\n return [\n \"ADJ\",\n \"ADP\",\n \"ADV\",\n \"AUX\",\n \"CCONJ\",\n \"DET\",\n \"INTJ\",\n \"NOUN\",\n \"NUM\",\n \"PART\",\n \"PRON\",\n \"PROPN\",\n \"PUNCT\",","source_hash":"6c47ec218bcb6c0bc4fe1fc726384a91ab255e0f750aa4e0b1ee2dee7c549579","truncated":false} {"repo_id":"CodeT","entity_id":"py:DIVERSE.code.src.tasks.get_labels","uri":"program://CodeT/function/DIVERSE.code.src.tasks.get_labels#L140-L163","kind":"function","name":"get_labels","path":"DIVERSE/code/src/tasks.py","language":"python","start_line":140,"end_line":163,"context_start_line":120,"context_end_line":163,"code":" for token in sentence:\n words.append(token[\"form\"])\n labels.append(token[\"upos\"])\n assert len(words) == len(labels)\n if words:\n examples.append(InputExample(guid=f\"{mode}-{guid_index}\", words=words, labels=labels))\n guid_index += 1\n return examples\n\n def write_predictions_to_file(self, writer: TextIO, test_input_reader: TextIO, preds_list: List):\n example_id = 0\n for sentence in parse_incr(test_input_reader):\n s_p = preds_list[example_id]\n out = \"\"\n for token in sentence:\n out += f'{token[\"form\"]} ({token[\"upos\"]}|{s_p.pop(0)}) '\n out += \"\\n\"\n writer.write(out)\n example_id += 1\n\n def get_labels(self, path: str) -> List[str]:\n if path:\n with open(path, \"r\") as f:\n return f.read().splitlines()\n else:\n return [\n \"ADJ\",\n \"ADP\",\n \"ADV\",\n \"AUX\",\n \"CCONJ\",\n \"DET\",\n \"INTJ\",\n \"NOUN\",\n \"NUM\",\n \"PART\",\n \"PRON\",\n \"PROPN\",\n \"PUNCT\",\n \"SCONJ\",\n \"SYM\",\n \"VERB\",\n \"X\",\n ]","source_hash":"6c47ec218bcb6c0bc4fe1fc726384a91ab255e0f750aa4e0b1ee2dee7c549579","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.main","uri":"program://CodeT/module/CodeT.main#L1-L49","kind":"module","name":"CodeT.main","path":"CodeT/main.py","language":"python","start_line":1,"end_line":49,"context_start_line":1,"context_end_line":49,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport argparse\nimport logging\nimport os\n\nfrom src.postprocess import PostProcessor\nfrom src.execution import evaluate_with_test_code, evaluate_with_test_cases\nfrom src.io_utils import Tools\nfrom src.agreement import DataManager, DualAgreement\nfrom src.evaluation import pass_at_K, get_result_of_sorted_solutions\n\nlogging.basicConfig(\n format=\"SystemLog: [%(asctime)s][%(name)s][%(levelname)s] - %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n level=logging.INFO,\n)\n\nlogger = logging.getLogger(__name__)\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--source_path_for_solution\", type=str, help=\"model input file in .jsonl format\")\n parser.add_argument(\"--predict_path_for_solution\", type=str, help=\"model output file in .jsonl format\")\n parser.add_argument(\"--source_path_for_test\", type=str, help=\"model input file in .jsonl format\")\n parser.add_argument(\"--predict_path_for_test\", type=str, help=\"model output file in .jsonl format\")\n parser.add_argument(\"--cache_dir\", type=str, help=\"the directory to store the cache files\")\n parser.add_argument(\"--timeout\", type=float, default=0.1, help=\"how many seconds to wait during execution for each test case\")\n parser.add_argument(\"--test_case_limit\", type=int, default=5, help=\"first n test cases per sample\")\n\n args = parser.parse_args()\n \n handled_solutions, task_count = PostProcessor.map_task_id_for_solution(args.predict_path_for_solution, args.source_path_for_solution)\n handled_test_cases = PostProcessor.map_task_id_for_test_case(args.predict_path_for_test, args.source_path_for_test)\n \n ground_truth_exec_result = evaluate_with_test_code(handled_solutions, timeout=args.timeout)\n dual_exec_result = evaluate_with_test_cases(handled_solutions, handled_test_cases, timeout=args.timeout, limit=args.test_case_limit)\n \n Tools.dump_pickle(os.path.join(args.cache_dir, 'ground_truth_exec_result.pkl'), ground_truth_exec_result)\n Tools.dump_pickle(os.path.join(args.cache_dir, 'dual_exec_result.pkl'), dual_exec_result)\n \n data_manager = DataManager(dual_exec_result, handled_solutions, handled_test_cases, args.test_case_limit)\n set_consistency = DualAgreement(data_manager)\n ranked_result = set_consistency.get_sorted_solutions_without_iter()\n logger.info('pass rates of ranked solutions')\n get_result_of_sorted_solutions(ground_truth_exec_result, ranked_result)\n logger.info('pass rates of random solutions')\n pass_at_K(ground_truth_exec_result)","source_hash":"b635824dc6f4d0e6dc4ce3d913d5f769699efe9186b24ee37c5454e20efa1013","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.io_utils","uri":"program://CodeT/module/CodeT.src.io_utils#L1-L37","kind":"module","name":"CodeT.src.io_utils","path":"CodeT/src/io_utils.py","language":"python","start_line":1,"end_line":37,"context_start_line":1,"context_end_line":37,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport json\nimport pickle\n\nclass Tools:\n @staticmethod\n def load_jsonl(file_path):\n json_objects = []\n with open(file_path, 'r', encoding='utf8') as f:\n for line in f:\n json_objects.append(json.loads(line.strip()))\n return json_objects\n \n @staticmethod\n def load_tasks(task_path):\n result = dict()\n lines = Tools.load_jsonl(task_path)\n for line in lines:\n result[line['task_id']] = line\n return result\n \n @staticmethod\n def dump_pickle(path, content):\n with open(path, 'wb') as f:\n pickle.dump(content, f)\n \n @staticmethod\n def load_pickle(path):\n with open(path, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def write_file(path, content):\n with open(path, 'w', encoding='utf8') as f:\n f.write(content)","source_hash":"0c5eb6d103a379d4a7c961e4948df2ad3b612ff0f8e52c48cea0a8f2964eff2a","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.io_utils.Tools","uri":"program://CodeT/class/CodeT.src.io_utils.Tools#L7-L37","kind":"class","name":"Tools","path":"CodeT/src/io_utils.py","language":"python","start_line":7,"end_line":37,"context_start_line":1,"context_end_line":37,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport json\nimport pickle\n\nclass Tools:\n @staticmethod\n def load_jsonl(file_path):\n json_objects = []\n with open(file_path, 'r', encoding='utf8') as f:\n for line in f:\n json_objects.append(json.loads(line.strip()))\n return json_objects\n \n @staticmethod\n def load_tasks(task_path):\n result = dict()\n lines = Tools.load_jsonl(task_path)\n for line in lines:\n result[line['task_id']] = line\n return result\n \n @staticmethod\n def dump_pickle(path, content):\n with open(path, 'wb') as f:\n pickle.dump(content, f)\n \n @staticmethod\n def load_pickle(path):\n with open(path, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def write_file(path, content):\n with open(path, 'w', encoding='utf8') as f:\n f.write(content)","source_hash":"0c5eb6d103a379d4a7c961e4948df2ad3b612ff0f8e52c48cea0a8f2964eff2a","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.io_utils.load_jsonl","uri":"program://CodeT/function/CodeT.src.io_utils.load_jsonl#L9-L14","kind":"function","name":"load_jsonl","path":"CodeT/src/io_utils.py","language":"python","start_line":9,"end_line":14,"context_start_line":1,"context_end_line":34,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport json\nimport pickle\n\nclass Tools:\n @staticmethod\n def load_jsonl(file_path):\n json_objects = []\n with open(file_path, 'r', encoding='utf8') as f:\n for line in f:\n json_objects.append(json.loads(line.strip()))\n return json_objects\n \n @staticmethod\n def load_tasks(task_path):\n result = dict()\n lines = Tools.load_jsonl(task_path)\n for line in lines:\n result[line['task_id']] = line\n return result\n \n @staticmethod\n def dump_pickle(path, content):\n with open(path, 'wb') as f:\n pickle.dump(content, f)\n \n @staticmethod\n def load_pickle(path):\n with open(path, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod","source_hash":"0c5eb6d103a379d4a7c961e4948df2ad3b612ff0f8e52c48cea0a8f2964eff2a","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.io_utils.load_tasks","uri":"program://CodeT/function/CodeT.src.io_utils.load_tasks#L17-L22","kind":"function","name":"load_tasks","path":"CodeT/src/io_utils.py","language":"python","start_line":17,"end_line":22,"context_start_line":1,"context_end_line":37,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport json\nimport pickle\n\nclass Tools:\n @staticmethod\n def load_jsonl(file_path):\n json_objects = []\n with open(file_path, 'r', encoding='utf8') as f:\n for line in f:\n json_objects.append(json.loads(line.strip()))\n return json_objects\n \n @staticmethod\n def load_tasks(task_path):\n result = dict()\n lines = Tools.load_jsonl(task_path)\n for line in lines:\n result[line['task_id']] = line\n return result\n \n @staticmethod\n def dump_pickle(path, content):\n with open(path, 'wb') as f:\n pickle.dump(content, f)\n \n @staticmethod\n def load_pickle(path):\n with open(path, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def write_file(path, content):\n with open(path, 'w', encoding='utf8') as f:\n f.write(content)","source_hash":"0c5eb6d103a379d4a7c961e4948df2ad3b612ff0f8e52c48cea0a8f2964eff2a","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.io_utils.dump_pickle","uri":"program://CodeT/function/CodeT.src.io_utils.dump_pickle#L25-L27","kind":"function","name":"dump_pickle","path":"CodeT/src/io_utils.py","language":"python","start_line":25,"end_line":27,"context_start_line":5,"context_end_line":37,"code":"import pickle\n\nclass Tools:\n @staticmethod\n def load_jsonl(file_path):\n json_objects = []\n with open(file_path, 'r', encoding='utf8') as f:\n for line in f:\n json_objects.append(json.loads(line.strip()))\n return json_objects\n \n @staticmethod\n def load_tasks(task_path):\n result = dict()\n lines = Tools.load_jsonl(task_path)\n for line in lines:\n result[line['task_id']] = line\n return result\n \n @staticmethod\n def dump_pickle(path, content):\n with open(path, 'wb') as f:\n pickle.dump(content, f)\n \n @staticmethod\n def load_pickle(path):\n with open(path, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def write_file(path, content):\n with open(path, 'w', encoding='utf8') as f:\n f.write(content)","source_hash":"0c5eb6d103a379d4a7c961e4948df2ad3b612ff0f8e52c48cea0a8f2964eff2a","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.io_utils.load_pickle","uri":"program://CodeT/function/CodeT.src.io_utils.load_pickle#L30-L32","kind":"function","name":"load_pickle","path":"CodeT/src/io_utils.py","language":"python","start_line":30,"end_line":32,"context_start_line":10,"context_end_line":37,"code":" json_objects = []\n with open(file_path, 'r', encoding='utf8') as f:\n for line in f:\n json_objects.append(json.loads(line.strip()))\n return json_objects\n \n @staticmethod\n def load_tasks(task_path):\n result = dict()\n lines = Tools.load_jsonl(task_path)\n for line in lines:\n result[line['task_id']] = line\n return result\n \n @staticmethod\n def dump_pickle(path, content):\n with open(path, 'wb') as f:\n pickle.dump(content, f)\n \n @staticmethod\n def load_pickle(path):\n with open(path, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def write_file(path, content):\n with open(path, 'w', encoding='utf8') as f:\n f.write(content)","source_hash":"0c5eb6d103a379d4a7c961e4948df2ad3b612ff0f8e52c48cea0a8f2964eff2a","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.io_utils.write_file","uri":"program://CodeT/function/CodeT.src.io_utils.write_file#L35-L37","kind":"function","name":"write_file","path":"CodeT/src/io_utils.py","language":"python","start_line":35,"end_line":37,"context_start_line":15,"context_end_line":37,"code":" \n @staticmethod\n def load_tasks(task_path):\n result = dict()\n lines = Tools.load_jsonl(task_path)\n for line in lines:\n result[line['task_id']] = line\n return result\n \n @staticmethod\n def dump_pickle(path, content):\n with open(path, 'wb') as f:\n pickle.dump(content, f)\n \n @staticmethod\n def load_pickle(path):\n with open(path, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def write_file(path, content):\n with open(path, 'w', encoding='utf8') as f:\n f.write(content)","source_hash":"0c5eb6d103a379d4a7c961e4948df2ad3b612ff0f8e52c48cea0a8f2964eff2a","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.agreement","uri":"program://CodeT/module/CodeT.src.agreement#L1-L158","kind":"module","name":"CodeT.src.agreement","path":"CodeT/src/agreement.py","language":"python","start_line":1,"end_line":158,"context_start_line":1,"context_end_line":158,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom collections import defaultdict, Counter\nimport logging\nimport math\n\n\nlogging.basicConfig(\n format=\"SystemLog: [%(asctime)s][%(name)s][%(levelname)s] - %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n level=logging.INFO,\n)\n\nlogger = logging.getLogger(__name__)\n\nclass DataManager:\n def __init__(self, dual_exec_results, sampled_code_by_task, sampled_test_case_by_task, limit):\n logger.info('handling dual exec results')\n self.dual_exec_results = dual_exec_results\n self.sampled_code_by_task = sampled_code_by_task\n self.sampled_test_case_by_task = sampled_test_case_by_task\n self.limit = limit\n \n self.solution_frequency_by_task = defaultdict(Counter)\n self.test_case_frequency_by_task = dict()\n self.passed_unique_solutions_by_task = defaultdict(set)\n self.passed_unique_test_cases_by_task = defaultdict(set)\n self.passed_solution_test_case_pairs_by_task = defaultdict(set)\n self.solution_string_to_id_range_by_task = dict()\n self.test_case_string_to_id_range_by_task = dict()\n self.solution_id_to_string_by_task = dict()\n self.test_case_id_to_string_by_task = dict()\n \n self.expanded_passed_solution_test_case_pairs_by_task = defaultdict(list)\n \n self._get_solution_frequency()\n logger.info('got solution frequency')\n self._get_test_case_frequency()\n logger.info('got test case frequency')\n self._get_passed_solution_test_case_pairs_by_task()\n logger.info('got passed solution test case pairs by task')\n self._get_solution_and_test_case_ids()\n logger.info('got solution and test case ids')\n self._get_expanded_dual_exec_result()\n logger.info('got expanded dual exec results')\n \n def _get_solution_frequency(self):\n for sample in self.sampled_code_by_task:\n task_id = sample['task_id']\n completion = sample['completion']\n self.solution_frequency_by_task[task_id][completion] += 1\n\n def _get_test_case_frequency(self):\n for task_id in self.sampled_test_case_by_task.keys():\n task_test_cases = [\n cases_per_sample[:self.limit] for cases_per_sample in self.sampled_test_case_by_task[task_id]\n ]\n task_test_cases = sum(task_test_cases, [])\n self.test_case_frequency_by_task[task_id] = Counter(task_test_cases)\n \n def _get_passed_solution_test_case_pairs_by_task(self):\n for result in self.dual_exec_results:\n if not result['passed']:\n continue\n for idx, test_case in enumerate(result['test_cases']):\n if result['result'][idx] != True:\n continue\n if test_case not in self.test_case_frequency_by_task[result['task_id']]:\n continue\n self.passed_solution_test_case_pairs_by_task[result['task_id']].add((result['completion'], test_case))\n self.passed_unique_solutions_by_task[result['task_id']].add(result['completion'])\n self.passed_unique_test_cases_by_task[result['task_id']].add(test_case)\n\n def _build_string_to_id_range(self, frequency_dict, limited_values):\n id_ranges = dict()\n start_id = 0\n for key, value in frequency_dict.items():\n if key not in limited_values:\n continue\n id_ranges[key] = range(start_id, start_id + value)\n start_id += value\n return id_ranges\n \n def _build_id_to_string(self, str_to_id_range):\n id_to_string = dict()\n for string in str_to_id_range.keys():\n for idx in str_to_id_range[string]:\n id_to_string[idx] = string\n return id_to_string\n \n def _get_solution_and_test_case_ids(self):\n for task_id in self.solution_frequency_by_task.keys():\n self.solution_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.solution_frequency_by_task[task_id], self.passed_unique_solutions_by_task[task_id])\n self.test_case_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.test_case_frequency_by_task[task_id], self.passed_unique_test_cases_by_task[task_id])\n self.solution_id_to_string_by_task[task_id] = self._build_id_to_string(self.solution_string_to_id_range_by_task[task_id])\n self.test_case_id_to_string_by_task[task_id] = self._build_id_to_string(self.test_case_string_to_id_range_by_task[task_id])\n \n def _get_expanded_by_id_range(self, solution_id_range, test_case_id_range):\n result = list()\n for solution_id in solution_id_range:\n for test_case_id in test_case_id_range:\n result.append((solution_id, test_case_id))\n return result\n \n def _get_expanded_dual_exec_result(self):\n for task_id in self.passed_solution_test_case_pairs_by_task.keys():\n for solution_str, test_case_str in self.passed_solution_test_case_pairs_by_task[task_id]:\n solution_id_range = self.solution_string_to_id_range_by_task[task_id][solution_str]\n test_case_id_range = self.test_case_string_to_id_range_by_task[task_id][test_case_str]\n self.expanded_passed_solution_test_case_pairs_by_task[task_id] += self._get_expanded_by_id_range(solution_id_range, test_case_id_range)\n\n\nclass DualAgreement:\n def __init__(self, data_manager):\n self.data_manager = data_manager\n self.dual_exec_results_by_task = data_manager.expanded_passed_solution_test_case_pairs_by_task\n self.solution_id_to_string_by_task = data_manager.solution_id_to_string_by_task\n \n self.solution_passed_cases_by_task = defaultdict(defaultdict)\n self.caseset_passed_solutions_by_task = defaultdict(defaultdict)\n \n self._get_solution_passed_case_set()\n logger.info('got solution passed case sets')\n self._get_caseset_passed_solutions()\n logger.info('got case set passed solutions')\n \n def _get_solution_passed_case_set(self):\n for task_id in self.dual_exec_results_by_task:\n for solution, test_case in self.dual_exec_results_by_task[task_id]:\n if solution in self.solution_passed_cases_by_task[task_id]:\n self.solution_passed_cases_by_task[task_id][solution].append(test_case)\n else:\n self.solution_passed_cases_by_task[task_id][solution] = [test_case]\n\n def _get_caseset_passed_solutions(self):\n for task_id in self.solution_passed_cases_by_task.keys():\n for solution in self.solution_passed_cases_by_task[task_id].keys():\n case_set = tuple(sorted(self.solution_passed_cases_by_task[task_id][solution])) # case_set: set of (test_case, score)\n if case_set in self.caseset_passed_solutions_by_task[task_id]:\n self.caseset_passed_solutions_by_task[task_id][case_set].append(solution)\n else:\n self.caseset_passed_solutions_by_task[task_id][case_set] = [solution]\n \n def get_sorted_solutions_without_iter(self):\n logger.info('Start to get sorted solutions without iter')\n # caseset_passed_solutions = {task_id: {case_set: [solution]}}\n ranked_solutions_by_task = defaultdict(list)\n for task_id in self.caseset_passed_solutions_by_task.keys():\n flatted_case_set_passed_solutions = []\n for case_set in self.caseset_passed_solutions_by_task[task_id].keys():\n solution_set = self.caseset_passed_solutions_by_task[task_id][case_set]\n solution_set_score = math.sqrt(len(solution_set))\n case_set_score = len(case_set)\n solution_str_set = [self.solution_id_to_string_by_task[task_id][solution] for solution in solution_set]\n flatted_case_set_passed_solutions.append((solution_str_set, case_set_score*solution_set_score))\n ranked_solutions_by_task[task_id] = sorted(flatted_case_set_passed_solutions, key=lambda x: x[1], reverse=True)\n return ranked_solutions_by_task","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.agreement.DataManager","uri":"program://CodeT/class/CodeT.src.agreement.DataManager#L17-L111","kind":"class","name":"DataManager","path":"CodeT/src/agreement.py","language":"python","start_line":17,"end_line":111,"context_start_line":1,"context_end_line":131,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom collections import defaultdict, Counter\nimport logging\nimport math\n\n\nlogging.basicConfig(\n format=\"SystemLog: [%(asctime)s][%(name)s][%(levelname)s] - %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n level=logging.INFO,\n)\n\nlogger = logging.getLogger(__name__)\n\nclass DataManager:\n def __init__(self, dual_exec_results, sampled_code_by_task, sampled_test_case_by_task, limit):\n logger.info('handling dual exec results')\n self.dual_exec_results = dual_exec_results\n self.sampled_code_by_task = sampled_code_by_task\n self.sampled_test_case_by_task = sampled_test_case_by_task\n self.limit = limit\n \n self.solution_frequency_by_task = defaultdict(Counter)\n self.test_case_frequency_by_task = dict()\n self.passed_unique_solutions_by_task = defaultdict(set)\n self.passed_unique_test_cases_by_task = defaultdict(set)\n self.passed_solution_test_case_pairs_by_task = defaultdict(set)\n self.solution_string_to_id_range_by_task = dict()\n self.test_case_string_to_id_range_by_task = dict()\n self.solution_id_to_string_by_task = dict()\n self.test_case_id_to_string_by_task = dict()\n \n self.expanded_passed_solution_test_case_pairs_by_task = defaultdict(list)\n \n self._get_solution_frequency()\n logger.info('got solution frequency')\n self._get_test_case_frequency()\n logger.info('got test case frequency')\n self._get_passed_solution_test_case_pairs_by_task()\n logger.info('got passed solution test case pairs by task')\n self._get_solution_and_test_case_ids()\n logger.info('got solution and test case ids')\n self._get_expanded_dual_exec_result()\n logger.info('got expanded dual exec results')\n \n def _get_solution_frequency(self):\n for sample in self.sampled_code_by_task:\n task_id = sample['task_id']\n completion = sample['completion']\n self.solution_frequency_by_task[task_id][completion] += 1\n\n def _get_test_case_frequency(self):\n for task_id in self.sampled_test_case_by_task.keys():\n task_test_cases = [\n cases_per_sample[:self.limit] for cases_per_sample in self.sampled_test_case_by_task[task_id]\n ]\n task_test_cases = sum(task_test_cases, [])\n self.test_case_frequency_by_task[task_id] = Counter(task_test_cases)\n \n def _get_passed_solution_test_case_pairs_by_task(self):\n for result in self.dual_exec_results:\n if not result['passed']:\n continue\n for idx, test_case in enumerate(result['test_cases']):\n if result['result'][idx] != True:\n continue\n if test_case not in self.test_case_frequency_by_task[result['task_id']]:\n continue\n self.passed_solution_test_case_pairs_by_task[result['task_id']].add((result['completion'], test_case))\n self.passed_unique_solutions_by_task[result['task_id']].add(result['completion'])\n self.passed_unique_test_cases_by_task[result['task_id']].add(test_case)\n\n def _build_string_to_id_range(self, frequency_dict, limited_values):\n id_ranges = dict()\n start_id = 0\n for key, value in frequency_dict.items():\n if key not in limited_values:\n continue\n id_ranges[key] = range(start_id, start_id + value)\n start_id += value\n return id_ranges\n \n def _build_id_to_string(self, str_to_id_range):\n id_to_string = dict()\n for string in str_to_id_range.keys():\n for idx in str_to_id_range[string]:\n id_to_string[idx] = string\n return id_to_string\n \n def _get_solution_and_test_case_ids(self):\n for task_id in self.solution_frequency_by_task.keys():\n self.solution_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.solution_frequency_by_task[task_id], self.passed_unique_solutions_by_task[task_id])\n self.test_case_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.test_case_frequency_by_task[task_id], self.passed_unique_test_cases_by_task[task_id])\n self.solution_id_to_string_by_task[task_id] = self._build_id_to_string(self.solution_string_to_id_range_by_task[task_id])\n self.test_case_id_to_string_by_task[task_id] = self._build_id_to_string(self.test_case_string_to_id_range_by_task[task_id])\n \n def _get_expanded_by_id_range(self, solution_id_range, test_case_id_range):\n result = list()\n for solution_id in solution_id_range:\n for test_case_id in test_case_id_range:\n result.append((solution_id, test_case_id))\n return result\n \n def _get_expanded_dual_exec_result(self):\n for task_id in self.passed_solution_test_case_pairs_by_task.keys():\n for solution_str, test_case_str in self.passed_solution_test_case_pairs_by_task[task_id]:\n solution_id_range = self.solution_string_to_id_range_by_task[task_id][solution_str]\n test_case_id_range = self.test_case_string_to_id_range_by_task[task_id][test_case_str]\n self.expanded_passed_solution_test_case_pairs_by_task[task_id] += self._get_expanded_by_id_range(solution_id_range, test_case_id_range)\n\n\nclass DualAgreement:\n def __init__(self, data_manager):\n self.data_manager = data_manager\n self.dual_exec_results_by_task = data_manager.expanded_passed_solution_test_case_pairs_by_task\n self.solution_id_to_string_by_task = data_manager.solution_id_to_string_by_task\n \n self.solution_passed_cases_by_task = defaultdict(defaultdict)\n self.caseset_passed_solutions_by_task = defaultdict(defaultdict)\n \n self._get_solution_passed_case_set()\n logger.info('got solution passed case sets')\n self._get_caseset_passed_solutions()\n logger.info('got case set passed solutions')\n \n def _get_solution_passed_case_set(self):\n for task_id in self.dual_exec_results_by_task:\n for solution, test_case in self.dual_exec_results_by_task[task_id]:\n if solution in self.solution_passed_cases_by_task[task_id]:","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.agreement.DualAgreement","uri":"program://CodeT/class/CodeT.src.agreement.DualAgreement#L114-L158","kind":"class","name":"DualAgreement","path":"CodeT/src/agreement.py","language":"python","start_line":114,"end_line":158,"context_start_line":94,"context_end_line":158,"code":" self.solution_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.solution_frequency_by_task[task_id], self.passed_unique_solutions_by_task[task_id])\n self.test_case_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.test_case_frequency_by_task[task_id], self.passed_unique_test_cases_by_task[task_id])\n self.solution_id_to_string_by_task[task_id] = self._build_id_to_string(self.solution_string_to_id_range_by_task[task_id])\n self.test_case_id_to_string_by_task[task_id] = self._build_id_to_string(self.test_case_string_to_id_range_by_task[task_id])\n \n def _get_expanded_by_id_range(self, solution_id_range, test_case_id_range):\n result = list()\n for solution_id in solution_id_range:\n for test_case_id in test_case_id_range:\n result.append((solution_id, test_case_id))\n return result\n \n def _get_expanded_dual_exec_result(self):\n for task_id in self.passed_solution_test_case_pairs_by_task.keys():\n for solution_str, test_case_str in self.passed_solution_test_case_pairs_by_task[task_id]:\n solution_id_range = self.solution_string_to_id_range_by_task[task_id][solution_str]\n test_case_id_range = self.test_case_string_to_id_range_by_task[task_id][test_case_str]\n self.expanded_passed_solution_test_case_pairs_by_task[task_id] += self._get_expanded_by_id_range(solution_id_range, test_case_id_range)\n\n\nclass DualAgreement:\n def __init__(self, data_manager):\n self.data_manager = data_manager\n self.dual_exec_results_by_task = data_manager.expanded_passed_solution_test_case_pairs_by_task\n self.solution_id_to_string_by_task = data_manager.solution_id_to_string_by_task\n \n self.solution_passed_cases_by_task = defaultdict(defaultdict)\n self.caseset_passed_solutions_by_task = defaultdict(defaultdict)\n \n self._get_solution_passed_case_set()\n logger.info('got solution passed case sets')\n self._get_caseset_passed_solutions()\n logger.info('got case set passed solutions')\n \n def _get_solution_passed_case_set(self):\n for task_id in self.dual_exec_results_by_task:\n for solution, test_case in self.dual_exec_results_by_task[task_id]:\n if solution in self.solution_passed_cases_by_task[task_id]:\n self.solution_passed_cases_by_task[task_id][solution].append(test_case)\n else:\n self.solution_passed_cases_by_task[task_id][solution] = [test_case]\n\n def _get_caseset_passed_solutions(self):\n for task_id in self.solution_passed_cases_by_task.keys():\n for solution in self.solution_passed_cases_by_task[task_id].keys():\n case_set = tuple(sorted(self.solution_passed_cases_by_task[task_id][solution])) # case_set: set of (test_case, score)\n if case_set in self.caseset_passed_solutions_by_task[task_id]:\n self.caseset_passed_solutions_by_task[task_id][case_set].append(solution)\n else:\n self.caseset_passed_solutions_by_task[task_id][case_set] = [solution]\n \n def get_sorted_solutions_without_iter(self):\n logger.info('Start to get sorted solutions without iter')\n # caseset_passed_solutions = {task_id: {case_set: [solution]}}\n ranked_solutions_by_task = defaultdict(list)\n for task_id in self.caseset_passed_solutions_by_task.keys():\n flatted_case_set_passed_solutions = []\n for case_set in self.caseset_passed_solutions_by_task[task_id].keys():\n solution_set = self.caseset_passed_solutions_by_task[task_id][case_set]\n solution_set_score = math.sqrt(len(solution_set))\n case_set_score = len(case_set)\n solution_str_set = [self.solution_id_to_string_by_task[task_id][solution] for solution in solution_set]\n flatted_case_set_passed_solutions.append((solution_str_set, case_set_score*solution_set_score))\n ranked_solutions_by_task[task_id] = sorted(flatted_case_set_passed_solutions, key=lambda x: x[1], reverse=True)\n return ranked_solutions_by_task","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.agreement.__init__","uri":"program://CodeT/function/CodeT.src.agreement.__init__#L115-L126","kind":"function","name":"__init__","path":"CodeT/src/agreement.py","language":"python","start_line":115,"end_line":126,"context_start_line":95,"context_end_line":146,"code":" self.test_case_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.test_case_frequency_by_task[task_id], self.passed_unique_test_cases_by_task[task_id])\n self.solution_id_to_string_by_task[task_id] = self._build_id_to_string(self.solution_string_to_id_range_by_task[task_id])\n self.test_case_id_to_string_by_task[task_id] = self._build_id_to_string(self.test_case_string_to_id_range_by_task[task_id])\n \n def _get_expanded_by_id_range(self, solution_id_range, test_case_id_range):\n result = list()\n for solution_id in solution_id_range:\n for test_case_id in test_case_id_range:\n result.append((solution_id, test_case_id))\n return result\n \n def _get_expanded_dual_exec_result(self):\n for task_id in self.passed_solution_test_case_pairs_by_task.keys():\n for solution_str, test_case_str in self.passed_solution_test_case_pairs_by_task[task_id]:\n solution_id_range = self.solution_string_to_id_range_by_task[task_id][solution_str]\n test_case_id_range = self.test_case_string_to_id_range_by_task[task_id][test_case_str]\n self.expanded_passed_solution_test_case_pairs_by_task[task_id] += self._get_expanded_by_id_range(solution_id_range, test_case_id_range)\n\n\nclass DualAgreement:\n def __init__(self, data_manager):\n self.data_manager = data_manager\n self.dual_exec_results_by_task = data_manager.expanded_passed_solution_test_case_pairs_by_task\n self.solution_id_to_string_by_task = data_manager.solution_id_to_string_by_task\n \n self.solution_passed_cases_by_task = defaultdict(defaultdict)\n self.caseset_passed_solutions_by_task = defaultdict(defaultdict)\n \n self._get_solution_passed_case_set()\n logger.info('got solution passed case sets')\n self._get_caseset_passed_solutions()\n logger.info('got case set passed solutions')\n \n def _get_solution_passed_case_set(self):\n for task_id in self.dual_exec_results_by_task:\n for solution, test_case in self.dual_exec_results_by_task[task_id]:\n if solution in self.solution_passed_cases_by_task[task_id]:\n self.solution_passed_cases_by_task[task_id][solution].append(test_case)\n else:\n self.solution_passed_cases_by_task[task_id][solution] = [test_case]\n\n def _get_caseset_passed_solutions(self):\n for task_id in self.solution_passed_cases_by_task.keys():\n for solution in self.solution_passed_cases_by_task[task_id].keys():\n case_set = tuple(sorted(self.solution_passed_cases_by_task[task_id][solution])) # case_set: set of (test_case, score)\n if case_set in self.caseset_passed_solutions_by_task[task_id]:\n self.caseset_passed_solutions_by_task[task_id][case_set].append(solution)\n else:\n self.caseset_passed_solutions_by_task[task_id][case_set] = [solution]\n \n def get_sorted_solutions_without_iter(self):\n logger.info('Start to get sorted solutions without iter')","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.agreement._get_solution_frequency","uri":"program://CodeT/function/CodeT.src.agreement._get_solution_frequency#L48-L52","kind":"function","name":"_get_solution_frequency","path":"CodeT/src/agreement.py","language":"python","start_line":48,"end_line":52,"context_start_line":28,"context_end_line":72,"code":" self.passed_unique_test_cases_by_task = defaultdict(set)\n self.passed_solution_test_case_pairs_by_task = defaultdict(set)\n self.solution_string_to_id_range_by_task = dict()\n self.test_case_string_to_id_range_by_task = dict()\n self.solution_id_to_string_by_task = dict()\n self.test_case_id_to_string_by_task = dict()\n \n self.expanded_passed_solution_test_case_pairs_by_task = defaultdict(list)\n \n self._get_solution_frequency()\n logger.info('got solution frequency')\n self._get_test_case_frequency()\n logger.info('got test case frequency')\n self._get_passed_solution_test_case_pairs_by_task()\n logger.info('got passed solution test case pairs by task')\n self._get_solution_and_test_case_ids()\n logger.info('got solution and test case ids')\n self._get_expanded_dual_exec_result()\n logger.info('got expanded dual exec results')\n \n def _get_solution_frequency(self):\n for sample in self.sampled_code_by_task:\n task_id = sample['task_id']\n completion = sample['completion']\n self.solution_frequency_by_task[task_id][completion] += 1\n\n def _get_test_case_frequency(self):\n for task_id in self.sampled_test_case_by_task.keys():\n task_test_cases = [\n cases_per_sample[:self.limit] for cases_per_sample in self.sampled_test_case_by_task[task_id]\n ]\n task_test_cases = sum(task_test_cases, [])\n self.test_case_frequency_by_task[task_id] = Counter(task_test_cases)\n \n def _get_passed_solution_test_case_pairs_by_task(self):\n for result in self.dual_exec_results:\n if not result['passed']:\n continue\n for idx, test_case in enumerate(result['test_cases']):\n if result['result'][idx] != True:\n continue\n if test_case not in self.test_case_frequency_by_task[result['task_id']]:\n continue\n self.passed_solution_test_case_pairs_by_task[result['task_id']].add((result['completion'], test_case))\n self.passed_unique_solutions_by_task[result['task_id']].add(result['completion'])","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.agreement._get_test_case_frequency","uri":"program://CodeT/function/CodeT.src.agreement._get_test_case_frequency#L54-L60","kind":"function","name":"_get_test_case_frequency","path":"CodeT/src/agreement.py","language":"python","start_line":54,"end_line":60,"context_start_line":34,"context_end_line":80,"code":" \n self.expanded_passed_solution_test_case_pairs_by_task = defaultdict(list)\n \n self._get_solution_frequency()\n logger.info('got solution frequency')\n self._get_test_case_frequency()\n logger.info('got test case frequency')\n self._get_passed_solution_test_case_pairs_by_task()\n logger.info('got passed solution test case pairs by task')\n self._get_solution_and_test_case_ids()\n logger.info('got solution and test case ids')\n self._get_expanded_dual_exec_result()\n logger.info('got expanded dual exec results')\n \n def _get_solution_frequency(self):\n for sample in self.sampled_code_by_task:\n task_id = sample['task_id']\n completion = sample['completion']\n self.solution_frequency_by_task[task_id][completion] += 1\n\n def _get_test_case_frequency(self):\n for task_id in self.sampled_test_case_by_task.keys():\n task_test_cases = [\n cases_per_sample[:self.limit] for cases_per_sample in self.sampled_test_case_by_task[task_id]\n ]\n task_test_cases = sum(task_test_cases, [])\n self.test_case_frequency_by_task[task_id] = Counter(task_test_cases)\n \n def _get_passed_solution_test_case_pairs_by_task(self):\n for result in self.dual_exec_results:\n if not result['passed']:\n continue\n for idx, test_case in enumerate(result['test_cases']):\n if result['result'][idx] != True:\n continue\n if test_case not in self.test_case_frequency_by_task[result['task_id']]:\n continue\n self.passed_solution_test_case_pairs_by_task[result['task_id']].add((result['completion'], test_case))\n self.passed_unique_solutions_by_task[result['task_id']].add(result['completion'])\n self.passed_unique_test_cases_by_task[result['task_id']].add(test_case)\n\n def _build_string_to_id_range(self, frequency_dict, limited_values):\n id_ranges = dict()\n start_id = 0\n for key, value in frequency_dict.items():\n if key not in limited_values:\n continue","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.agreement._get_passed_solution_test_case_pairs_by_task","uri":"program://CodeT/function/CodeT.src.agreement._get_passed_solution_test_case_pairs_by_task#L62-L73","kind":"function","name":"_get_passed_solution_test_case_pairs_by_task","path":"CodeT/src/agreement.py","language":"python","start_line":62,"end_line":73,"context_start_line":42,"context_end_line":93,"code":" logger.info('got passed solution test case pairs by task')\n self._get_solution_and_test_case_ids()\n logger.info('got solution and test case ids')\n self._get_expanded_dual_exec_result()\n logger.info('got expanded dual exec results')\n \n def _get_solution_frequency(self):\n for sample in self.sampled_code_by_task:\n task_id = sample['task_id']\n completion = sample['completion']\n self.solution_frequency_by_task[task_id][completion] += 1\n\n def _get_test_case_frequency(self):\n for task_id in self.sampled_test_case_by_task.keys():\n task_test_cases = [\n cases_per_sample[:self.limit] for cases_per_sample in self.sampled_test_case_by_task[task_id]\n ]\n task_test_cases = sum(task_test_cases, [])\n self.test_case_frequency_by_task[task_id] = Counter(task_test_cases)\n \n def _get_passed_solution_test_case_pairs_by_task(self):\n for result in self.dual_exec_results:\n if not result['passed']:\n continue\n for idx, test_case in enumerate(result['test_cases']):\n if result['result'][idx] != True:\n continue\n if test_case not in self.test_case_frequency_by_task[result['task_id']]:\n continue\n self.passed_solution_test_case_pairs_by_task[result['task_id']].add((result['completion'], test_case))\n self.passed_unique_solutions_by_task[result['task_id']].add(result['completion'])\n self.passed_unique_test_cases_by_task[result['task_id']].add(test_case)\n\n def _build_string_to_id_range(self, frequency_dict, limited_values):\n id_ranges = dict()\n start_id = 0\n for key, value in frequency_dict.items():\n if key not in limited_values:\n continue\n id_ranges[key] = range(start_id, start_id + value)\n start_id += value\n return id_ranges\n \n def _build_id_to_string(self, str_to_id_range):\n id_to_string = dict()\n for string in str_to_id_range.keys():\n for idx in str_to_id_range[string]:\n id_to_string[idx] = string\n return id_to_string\n \n def _get_solution_and_test_case_ids(self):\n for task_id in self.solution_frequency_by_task.keys():","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.agreement._build_string_to_id_range","uri":"program://CodeT/function/CodeT.src.agreement._build_string_to_id_range#L75-L83","kind":"function","name":"_build_string_to_id_range","path":"CodeT/src/agreement.py","language":"python","start_line":75,"end_line":83,"context_start_line":55,"context_end_line":103,"code":" for task_id in self.sampled_test_case_by_task.keys():\n task_test_cases = [\n cases_per_sample[:self.limit] for cases_per_sample in self.sampled_test_case_by_task[task_id]\n ]\n task_test_cases = sum(task_test_cases, [])\n self.test_case_frequency_by_task[task_id] = Counter(task_test_cases)\n \n def _get_passed_solution_test_case_pairs_by_task(self):\n for result in self.dual_exec_results:\n if not result['passed']:\n continue\n for idx, test_case in enumerate(result['test_cases']):\n if result['result'][idx] != True:\n continue\n if test_case not in self.test_case_frequency_by_task[result['task_id']]:\n continue\n self.passed_solution_test_case_pairs_by_task[result['task_id']].add((result['completion'], test_case))\n self.passed_unique_solutions_by_task[result['task_id']].add(result['completion'])\n self.passed_unique_test_cases_by_task[result['task_id']].add(test_case)\n\n def _build_string_to_id_range(self, frequency_dict, limited_values):\n id_ranges = dict()\n start_id = 0\n for key, value in frequency_dict.items():\n if key not in limited_values:\n continue\n id_ranges[key] = range(start_id, start_id + value)\n start_id += value\n return id_ranges\n \n def _build_id_to_string(self, str_to_id_range):\n id_to_string = dict()\n for string in str_to_id_range.keys():\n for idx in str_to_id_range[string]:\n id_to_string[idx] = string\n return id_to_string\n \n def _get_solution_and_test_case_ids(self):\n for task_id in self.solution_frequency_by_task.keys():\n self.solution_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.solution_frequency_by_task[task_id], self.passed_unique_solutions_by_task[task_id])\n self.test_case_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.test_case_frequency_by_task[task_id], self.passed_unique_test_cases_by_task[task_id])\n self.solution_id_to_string_by_task[task_id] = self._build_id_to_string(self.solution_string_to_id_range_by_task[task_id])\n self.test_case_id_to_string_by_task[task_id] = self._build_id_to_string(self.test_case_string_to_id_range_by_task[task_id])\n \n def _get_expanded_by_id_range(self, solution_id_range, test_case_id_range):\n result = list()\n for solution_id in solution_id_range:\n for test_case_id in test_case_id_range:\n result.append((solution_id, test_case_id))","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.agreement._build_id_to_string","uri":"program://CodeT/function/CodeT.src.agreement._build_id_to_string#L85-L90","kind":"function","name":"_build_id_to_string","path":"CodeT/src/agreement.py","language":"python","start_line":85,"end_line":90,"context_start_line":65,"context_end_line":110,"code":" continue\n for idx, test_case in enumerate(result['test_cases']):\n if result['result'][idx] != True:\n continue\n if test_case not in self.test_case_frequency_by_task[result['task_id']]:\n continue\n self.passed_solution_test_case_pairs_by_task[result['task_id']].add((result['completion'], test_case))\n self.passed_unique_solutions_by_task[result['task_id']].add(result['completion'])\n self.passed_unique_test_cases_by_task[result['task_id']].add(test_case)\n\n def _build_string_to_id_range(self, frequency_dict, limited_values):\n id_ranges = dict()\n start_id = 0\n for key, value in frequency_dict.items():\n if key not in limited_values:\n continue\n id_ranges[key] = range(start_id, start_id + value)\n start_id += value\n return id_ranges\n \n def _build_id_to_string(self, str_to_id_range):\n id_to_string = dict()\n for string in str_to_id_range.keys():\n for idx in str_to_id_range[string]:\n id_to_string[idx] = string\n return id_to_string\n \n def _get_solution_and_test_case_ids(self):\n for task_id in self.solution_frequency_by_task.keys():\n self.solution_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.solution_frequency_by_task[task_id], self.passed_unique_solutions_by_task[task_id])\n self.test_case_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.test_case_frequency_by_task[task_id], self.passed_unique_test_cases_by_task[task_id])\n self.solution_id_to_string_by_task[task_id] = self._build_id_to_string(self.solution_string_to_id_range_by_task[task_id])\n self.test_case_id_to_string_by_task[task_id] = self._build_id_to_string(self.test_case_string_to_id_range_by_task[task_id])\n \n def _get_expanded_by_id_range(self, solution_id_range, test_case_id_range):\n result = list()\n for solution_id in solution_id_range:\n for test_case_id in test_case_id_range:\n result.append((solution_id, test_case_id))\n return result\n \n def _get_expanded_dual_exec_result(self):\n for task_id in self.passed_solution_test_case_pairs_by_task.keys():\n for solution_str, test_case_str in self.passed_solution_test_case_pairs_by_task[task_id]:\n solution_id_range = self.solution_string_to_id_range_by_task[task_id][solution_str]\n test_case_id_range = self.test_case_string_to_id_range_by_task[task_id][test_case_str]","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.agreement._get_solution_and_test_case_ids","uri":"program://CodeT/function/CodeT.src.agreement._get_solution_and_test_case_ids#L92-L97","kind":"function","name":"_get_solution_and_test_case_ids","path":"CodeT/src/agreement.py","language":"python","start_line":92,"end_line":97,"context_start_line":72,"context_end_line":117,"code":" self.passed_unique_solutions_by_task[result['task_id']].add(result['completion'])\n self.passed_unique_test_cases_by_task[result['task_id']].add(test_case)\n\n def _build_string_to_id_range(self, frequency_dict, limited_values):\n id_ranges = dict()\n start_id = 0\n for key, value in frequency_dict.items():\n if key not in limited_values:\n continue\n id_ranges[key] = range(start_id, start_id + value)\n start_id += value\n return id_ranges\n \n def _build_id_to_string(self, str_to_id_range):\n id_to_string = dict()\n for string in str_to_id_range.keys():\n for idx in str_to_id_range[string]:\n id_to_string[idx] = string\n return id_to_string\n \n def _get_solution_and_test_case_ids(self):\n for task_id in self.solution_frequency_by_task.keys():\n self.solution_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.solution_frequency_by_task[task_id], self.passed_unique_solutions_by_task[task_id])\n self.test_case_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.test_case_frequency_by_task[task_id], self.passed_unique_test_cases_by_task[task_id])\n self.solution_id_to_string_by_task[task_id] = self._build_id_to_string(self.solution_string_to_id_range_by_task[task_id])\n self.test_case_id_to_string_by_task[task_id] = self._build_id_to_string(self.test_case_string_to_id_range_by_task[task_id])\n \n def _get_expanded_by_id_range(self, solution_id_range, test_case_id_range):\n result = list()\n for solution_id in solution_id_range:\n for test_case_id in test_case_id_range:\n result.append((solution_id, test_case_id))\n return result\n \n def _get_expanded_dual_exec_result(self):\n for task_id in self.passed_solution_test_case_pairs_by_task.keys():\n for solution_str, test_case_str in self.passed_solution_test_case_pairs_by_task[task_id]:\n solution_id_range = self.solution_string_to_id_range_by_task[task_id][solution_str]\n test_case_id_range = self.test_case_string_to_id_range_by_task[task_id][test_case_str]\n self.expanded_passed_solution_test_case_pairs_by_task[task_id] += self._get_expanded_by_id_range(solution_id_range, test_case_id_range)\n\n\nclass DualAgreement:\n def __init__(self, data_manager):\n self.data_manager = data_manager\n self.dual_exec_results_by_task = data_manager.expanded_passed_solution_test_case_pairs_by_task","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.agreement._get_expanded_by_id_range","uri":"program://CodeT/function/CodeT.src.agreement._get_expanded_by_id_range#L99-L104","kind":"function","name":"_get_expanded_by_id_range","path":"CodeT/src/agreement.py","language":"python","start_line":99,"end_line":104,"context_start_line":79,"context_end_line":124,"code":" if key not in limited_values:\n continue\n id_ranges[key] = range(start_id, start_id + value)\n start_id += value\n return id_ranges\n \n def _build_id_to_string(self, str_to_id_range):\n id_to_string = dict()\n for string in str_to_id_range.keys():\n for idx in str_to_id_range[string]:\n id_to_string[idx] = string\n return id_to_string\n \n def _get_solution_and_test_case_ids(self):\n for task_id in self.solution_frequency_by_task.keys():\n self.solution_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.solution_frequency_by_task[task_id], self.passed_unique_solutions_by_task[task_id])\n self.test_case_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.test_case_frequency_by_task[task_id], self.passed_unique_test_cases_by_task[task_id])\n self.solution_id_to_string_by_task[task_id] = self._build_id_to_string(self.solution_string_to_id_range_by_task[task_id])\n self.test_case_id_to_string_by_task[task_id] = self._build_id_to_string(self.test_case_string_to_id_range_by_task[task_id])\n \n def _get_expanded_by_id_range(self, solution_id_range, test_case_id_range):\n result = list()\n for solution_id in solution_id_range:\n for test_case_id in test_case_id_range:\n result.append((solution_id, test_case_id))\n return result\n \n def _get_expanded_dual_exec_result(self):\n for task_id in self.passed_solution_test_case_pairs_by_task.keys():\n for solution_str, test_case_str in self.passed_solution_test_case_pairs_by_task[task_id]:\n solution_id_range = self.solution_string_to_id_range_by_task[task_id][solution_str]\n test_case_id_range = self.test_case_string_to_id_range_by_task[task_id][test_case_str]\n self.expanded_passed_solution_test_case_pairs_by_task[task_id] += self._get_expanded_by_id_range(solution_id_range, test_case_id_range)\n\n\nclass DualAgreement:\n def __init__(self, data_manager):\n self.data_manager = data_manager\n self.dual_exec_results_by_task = data_manager.expanded_passed_solution_test_case_pairs_by_task\n self.solution_id_to_string_by_task = data_manager.solution_id_to_string_by_task\n \n self.solution_passed_cases_by_task = defaultdict(defaultdict)\n self.caseset_passed_solutions_by_task = defaultdict(defaultdict)\n \n self._get_solution_passed_case_set()\n logger.info('got solution passed case sets')","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.agreement._get_expanded_dual_exec_result","uri":"program://CodeT/function/CodeT.src.agreement._get_expanded_dual_exec_result#L106-L111","kind":"function","name":"_get_expanded_dual_exec_result","path":"CodeT/src/agreement.py","language":"python","start_line":106,"end_line":111,"context_start_line":86,"context_end_line":131,"code":" id_to_string = dict()\n for string in str_to_id_range.keys():\n for idx in str_to_id_range[string]:\n id_to_string[idx] = string\n return id_to_string\n \n def _get_solution_and_test_case_ids(self):\n for task_id in self.solution_frequency_by_task.keys():\n self.solution_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.solution_frequency_by_task[task_id], self.passed_unique_solutions_by_task[task_id])\n self.test_case_string_to_id_range_by_task[task_id] = self._build_string_to_id_range(self.test_case_frequency_by_task[task_id], self.passed_unique_test_cases_by_task[task_id])\n self.solution_id_to_string_by_task[task_id] = self._build_id_to_string(self.solution_string_to_id_range_by_task[task_id])\n self.test_case_id_to_string_by_task[task_id] = self._build_id_to_string(self.test_case_string_to_id_range_by_task[task_id])\n \n def _get_expanded_by_id_range(self, solution_id_range, test_case_id_range):\n result = list()\n for solution_id in solution_id_range:\n for test_case_id in test_case_id_range:\n result.append((solution_id, test_case_id))\n return result\n \n def _get_expanded_dual_exec_result(self):\n for task_id in self.passed_solution_test_case_pairs_by_task.keys():\n for solution_str, test_case_str in self.passed_solution_test_case_pairs_by_task[task_id]:\n solution_id_range = self.solution_string_to_id_range_by_task[task_id][solution_str]\n test_case_id_range = self.test_case_string_to_id_range_by_task[task_id][test_case_str]\n self.expanded_passed_solution_test_case_pairs_by_task[task_id] += self._get_expanded_by_id_range(solution_id_range, test_case_id_range)\n\n\nclass DualAgreement:\n def __init__(self, data_manager):\n self.data_manager = data_manager\n self.dual_exec_results_by_task = data_manager.expanded_passed_solution_test_case_pairs_by_task\n self.solution_id_to_string_by_task = data_manager.solution_id_to_string_by_task\n \n self.solution_passed_cases_by_task = defaultdict(defaultdict)\n self.caseset_passed_solutions_by_task = defaultdict(defaultdict)\n \n self._get_solution_passed_case_set()\n logger.info('got solution passed case sets')\n self._get_caseset_passed_solutions()\n logger.info('got case set passed solutions')\n \n def _get_solution_passed_case_set(self):\n for task_id in self.dual_exec_results_by_task:\n for solution, test_case in self.dual_exec_results_by_task[task_id]:\n if solution in self.solution_passed_cases_by_task[task_id]:","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.agreement._get_solution_passed_case_set","uri":"program://CodeT/function/CodeT.src.agreement._get_solution_passed_case_set#L128-L134","kind":"function","name":"_get_solution_passed_case_set","path":"CodeT/src/agreement.py","language":"python","start_line":128,"end_line":134,"context_start_line":108,"context_end_line":154,"code":" for solution_str, test_case_str in self.passed_solution_test_case_pairs_by_task[task_id]:\n solution_id_range = self.solution_string_to_id_range_by_task[task_id][solution_str]\n test_case_id_range = self.test_case_string_to_id_range_by_task[task_id][test_case_str]\n self.expanded_passed_solution_test_case_pairs_by_task[task_id] += self._get_expanded_by_id_range(solution_id_range, test_case_id_range)\n\n\nclass DualAgreement:\n def __init__(self, data_manager):\n self.data_manager = data_manager\n self.dual_exec_results_by_task = data_manager.expanded_passed_solution_test_case_pairs_by_task\n self.solution_id_to_string_by_task = data_manager.solution_id_to_string_by_task\n \n self.solution_passed_cases_by_task = defaultdict(defaultdict)\n self.caseset_passed_solutions_by_task = defaultdict(defaultdict)\n \n self._get_solution_passed_case_set()\n logger.info('got solution passed case sets')\n self._get_caseset_passed_solutions()\n logger.info('got case set passed solutions')\n \n def _get_solution_passed_case_set(self):\n for task_id in self.dual_exec_results_by_task:\n for solution, test_case in self.dual_exec_results_by_task[task_id]:\n if solution in self.solution_passed_cases_by_task[task_id]:\n self.solution_passed_cases_by_task[task_id][solution].append(test_case)\n else:\n self.solution_passed_cases_by_task[task_id][solution] = [test_case]\n\n def _get_caseset_passed_solutions(self):\n for task_id in self.solution_passed_cases_by_task.keys():\n for solution in self.solution_passed_cases_by_task[task_id].keys():\n case_set = tuple(sorted(self.solution_passed_cases_by_task[task_id][solution])) # case_set: set of (test_case, score)\n if case_set in self.caseset_passed_solutions_by_task[task_id]:\n self.caseset_passed_solutions_by_task[task_id][case_set].append(solution)\n else:\n self.caseset_passed_solutions_by_task[task_id][case_set] = [solution]\n \n def get_sorted_solutions_without_iter(self):\n logger.info('Start to get sorted solutions without iter')\n # caseset_passed_solutions = {task_id: {case_set: [solution]}}\n ranked_solutions_by_task = defaultdict(list)\n for task_id in self.caseset_passed_solutions_by_task.keys():\n flatted_case_set_passed_solutions = []\n for case_set in self.caseset_passed_solutions_by_task[task_id].keys():\n solution_set = self.caseset_passed_solutions_by_task[task_id][case_set]\n solution_set_score = math.sqrt(len(solution_set))\n case_set_score = len(case_set)","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.agreement._get_caseset_passed_solutions","uri":"program://CodeT/function/CodeT.src.agreement._get_caseset_passed_solutions#L136-L143","kind":"function","name":"_get_caseset_passed_solutions","path":"CodeT/src/agreement.py","language":"python","start_line":136,"end_line":143,"context_start_line":116,"context_end_line":158,"code":" self.data_manager = data_manager\n self.dual_exec_results_by_task = data_manager.expanded_passed_solution_test_case_pairs_by_task\n self.solution_id_to_string_by_task = data_manager.solution_id_to_string_by_task\n \n self.solution_passed_cases_by_task = defaultdict(defaultdict)\n self.caseset_passed_solutions_by_task = defaultdict(defaultdict)\n \n self._get_solution_passed_case_set()\n logger.info('got solution passed case sets')\n self._get_caseset_passed_solutions()\n logger.info('got case set passed solutions')\n \n def _get_solution_passed_case_set(self):\n for task_id in self.dual_exec_results_by_task:\n for solution, test_case in self.dual_exec_results_by_task[task_id]:\n if solution in self.solution_passed_cases_by_task[task_id]:\n self.solution_passed_cases_by_task[task_id][solution].append(test_case)\n else:\n self.solution_passed_cases_by_task[task_id][solution] = [test_case]\n\n def _get_caseset_passed_solutions(self):\n for task_id in self.solution_passed_cases_by_task.keys():\n for solution in self.solution_passed_cases_by_task[task_id].keys():\n case_set = tuple(sorted(self.solution_passed_cases_by_task[task_id][solution])) # case_set: set of (test_case, score)\n if case_set in self.caseset_passed_solutions_by_task[task_id]:\n self.caseset_passed_solutions_by_task[task_id][case_set].append(solution)\n else:\n self.caseset_passed_solutions_by_task[task_id][case_set] = [solution]\n \n def get_sorted_solutions_without_iter(self):\n logger.info('Start to get sorted solutions without iter')\n # caseset_passed_solutions = {task_id: {case_set: [solution]}}\n ranked_solutions_by_task = defaultdict(list)\n for task_id in self.caseset_passed_solutions_by_task.keys():\n flatted_case_set_passed_solutions = []\n for case_set in self.caseset_passed_solutions_by_task[task_id].keys():\n solution_set = self.caseset_passed_solutions_by_task[task_id][case_set]\n solution_set_score = math.sqrt(len(solution_set))\n case_set_score = len(case_set)\n solution_str_set = [self.solution_id_to_string_by_task[task_id][solution] for solution in solution_set]\n flatted_case_set_passed_solutions.append((solution_str_set, case_set_score*solution_set_score))\n ranked_solutions_by_task[task_id] = sorted(flatted_case_set_passed_solutions, key=lambda x: x[1], reverse=True)\n return ranked_solutions_by_task","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.agreement.get_sorted_solutions_without_iter","uri":"program://CodeT/function/CodeT.src.agreement.get_sorted_solutions_without_iter#L145-L158","kind":"function","name":"get_sorted_solutions_without_iter","path":"CodeT/src/agreement.py","language":"python","start_line":145,"end_line":158,"context_start_line":125,"context_end_line":158,"code":" self._get_caseset_passed_solutions()\n logger.info('got case set passed solutions')\n \n def _get_solution_passed_case_set(self):\n for task_id in self.dual_exec_results_by_task:\n for solution, test_case in self.dual_exec_results_by_task[task_id]:\n if solution in self.solution_passed_cases_by_task[task_id]:\n self.solution_passed_cases_by_task[task_id][solution].append(test_case)\n else:\n self.solution_passed_cases_by_task[task_id][solution] = [test_case]\n\n def _get_caseset_passed_solutions(self):\n for task_id in self.solution_passed_cases_by_task.keys():\n for solution in self.solution_passed_cases_by_task[task_id].keys():\n case_set = tuple(sorted(self.solution_passed_cases_by_task[task_id][solution])) # case_set: set of (test_case, score)\n if case_set in self.caseset_passed_solutions_by_task[task_id]:\n self.caseset_passed_solutions_by_task[task_id][case_set].append(solution)\n else:\n self.caseset_passed_solutions_by_task[task_id][case_set] = [solution]\n \n def get_sorted_solutions_without_iter(self):\n logger.info('Start to get sorted solutions without iter')\n # caseset_passed_solutions = {task_id: {case_set: [solution]}}\n ranked_solutions_by_task = defaultdict(list)\n for task_id in self.caseset_passed_solutions_by_task.keys():\n flatted_case_set_passed_solutions = []\n for case_set in self.caseset_passed_solutions_by_task[task_id].keys():\n solution_set = self.caseset_passed_solutions_by_task[task_id][case_set]\n solution_set_score = math.sqrt(len(solution_set))\n case_set_score = len(case_set)\n solution_str_set = [self.solution_id_to_string_by_task[task_id][solution] for solution in solution_set]\n flatted_case_set_passed_solutions.append((solution_str_set, case_set_score*solution_set_score))\n ranked_solutions_by_task[task_id] = sorted(flatted_case_set_passed_solutions, key=lambda x: x[1], reverse=True)\n return ranked_solutions_by_task","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.postprocess","uri":"program://CodeT/module/CodeT.src.postprocess#L1-L89","kind":"module","name":"CodeT.src.postprocess","path":"CodeT/src/postprocess.py","language":"python","start_line":1,"end_line":89,"context_start_line":1,"context_end_line":89,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom collections import defaultdict\n\nfrom src.io_utils import Tools\n\nSTOP_TOKEN = ['\\nclass', '\\ndef', '\\n#', '\\nif', '\\nprint']\n\nclass PostProcessor:\n @staticmethod\n def map_task_id_for_solution(predict_path, source_path):\n database = dict()\n raw_problems = Tools.load_tasks(source_path)\n for task_id in raw_problems.keys():\n database[raw_problems[task_id]['prompt']] = raw_problems[task_id]\n\n result = []\n predictions = Tools.load_jsonl(predict_path)\n for pre in predictions:\n task = database[pre['prompt']]\n if not pre['samples']:\n result.append({\n 'task_id': task['task_id'],\n 'prompt': pre['prompt'],\n 'test': task['test'],\n 'entry_point': task['entry_point'],\n 'completion': 'empty solution here, execution will fail'\n })\n for sample in pre['samples']:\n processed_code = PostProcessor.solution_extract(sample)\n result.append({\n 'task_id': task['task_id'],\n 'prompt': pre['prompt'],\n 'test': task['test'],\n 'entry_point': task['entry_point'],\n 'completion': processed_code\n })\n return result, len(raw_problems)\n\n @staticmethod\n def map_task_id_for_test_case(predict_path, source_path):\n database = dict()\n raw_problems = Tools.load_tasks(source_path)\n for task_id in raw_problems.keys():\n database[raw_problems[task_id]['prompt']] = raw_problems[task_id]\n\n test_cases_by_task = defaultdict(list)\n predictions = Tools.load_jsonl(predict_path)\n for pre in predictions:\n task = database[pre['prompt']]\n for sample in pre['samples']:\n test_cases = PostProcessor.test_case_extract(sample, task['entry_point'])\n test_cases_by_task[task['task_id']].append(test_cases)\n return test_cases_by_task\n\n @staticmethod\n def solution_extract(content):\n for identifier in STOP_TOKEN:\n if identifier in content:\n content = content.split(identifier)[0]\n return content\n \n @staticmethod\n def test_case_extract(content, entry_point):\n def _truncate(content):\n for identifier in STOP_TOKEN:\n if identifier in content:\n content = content.split(identifier)[0]\n return content.strip()\n \n split_by_assert = [f'assert {part}'.strip() for part in f'assert {content}'.split('assert ') if (entry_point.strip() in part) and len(part.strip()) > 0]\n truncated_test_cases = [_truncate(i) for i in split_by_assert]\n checked_assertions = [i for i in truncated_test_cases if PostProcessor._check_test_case_validation(i)]\n return checked_assertions\n\n @staticmethod\n def _check_test_case_validation(test_case):\n if len(test_case.strip()) < 1:\n return False\n if 'assert' not in test_case:\n return False\n try:\n multi_line_test_case = test_case.replace(\"\\n\", \"\\n \")\n assert_in_a_block = f'try:\\n {multi_line_test_case}\\nexcept:\\n pass\\n'\n compile(assert_in_a_block, '', 'exec')\n return True\n except Exception:\n return False","source_hash":"7ec392f825cb31702d88827a8fecba44178e785a13780d5f76db3332b1bd0331","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.postprocess.PostProcessor","uri":"program://CodeT/class/CodeT.src.postprocess.PostProcessor#L10-L89","kind":"class","name":"PostProcessor","path":"CodeT/src/postprocess.py","language":"python","start_line":10,"end_line":89,"context_start_line":1,"context_end_line":89,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom collections import defaultdict\n\nfrom src.io_utils import Tools\n\nSTOP_TOKEN = ['\\nclass', '\\ndef', '\\n#', '\\nif', '\\nprint']\n\nclass PostProcessor:\n @staticmethod\n def map_task_id_for_solution(predict_path, source_path):\n database = dict()\n raw_problems = Tools.load_tasks(source_path)\n for task_id in raw_problems.keys():\n database[raw_problems[task_id]['prompt']] = raw_problems[task_id]\n\n result = []\n predictions = Tools.load_jsonl(predict_path)\n for pre in predictions:\n task = database[pre['prompt']]\n if not pre['samples']:\n result.append({\n 'task_id': task['task_id'],\n 'prompt': pre['prompt'],\n 'test': task['test'],\n 'entry_point': task['entry_point'],\n 'completion': 'empty solution here, execution will fail'\n })\n for sample in pre['samples']:\n processed_code = PostProcessor.solution_extract(sample)\n result.append({\n 'task_id': task['task_id'],\n 'prompt': pre['prompt'],\n 'test': task['test'],\n 'entry_point': task['entry_point'],\n 'completion': processed_code\n })\n return result, len(raw_problems)\n\n @staticmethod\n def map_task_id_for_test_case(predict_path, source_path):\n database = dict()\n raw_problems = Tools.load_tasks(source_path)\n for task_id in raw_problems.keys():\n database[raw_problems[task_id]['prompt']] = raw_problems[task_id]\n\n test_cases_by_task = defaultdict(list)\n predictions = Tools.load_jsonl(predict_path)\n for pre in predictions:\n task = database[pre['prompt']]\n for sample in pre['samples']:\n test_cases = PostProcessor.test_case_extract(sample, task['entry_point'])\n test_cases_by_task[task['task_id']].append(test_cases)\n return test_cases_by_task\n\n @staticmethod\n def solution_extract(content):\n for identifier in STOP_TOKEN:\n if identifier in content:\n content = content.split(identifier)[0]\n return content\n \n @staticmethod\n def test_case_extract(content, entry_point):\n def _truncate(content):\n for identifier in STOP_TOKEN:\n if identifier in content:\n content = content.split(identifier)[0]\n return content.strip()\n \n split_by_assert = [f'assert {part}'.strip() for part in f'assert {content}'.split('assert ') if (entry_point.strip() in part) and len(part.strip()) > 0]\n truncated_test_cases = [_truncate(i) for i in split_by_assert]\n checked_assertions = [i for i in truncated_test_cases if PostProcessor._check_test_case_validation(i)]\n return checked_assertions\n\n @staticmethod\n def _check_test_case_validation(test_case):\n if len(test_case.strip()) < 1:\n return False\n if 'assert' not in test_case:\n return False\n try:\n multi_line_test_case = test_case.replace(\"\\n\", \"\\n \")\n assert_in_a_block = f'try:\\n {multi_line_test_case}\\nexcept:\\n pass\\n'\n compile(assert_in_a_block, '', 'exec')\n return True\n except Exception:\n return False","source_hash":"7ec392f825cb31702d88827a8fecba44178e785a13780d5f76db3332b1bd0331","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.postprocess.map_task_id_for_solution","uri":"program://CodeT/function/CodeT.src.postprocess.map_task_id_for_solution#L12-L39","kind":"function","name":"map_task_id_for_solution","path":"CodeT/src/postprocess.py","language":"python","start_line":12,"end_line":39,"context_start_line":1,"context_end_line":59,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom collections import defaultdict\n\nfrom src.io_utils import Tools\n\nSTOP_TOKEN = ['\\nclass', '\\ndef', '\\n#', '\\nif', '\\nprint']\n\nclass PostProcessor:\n @staticmethod\n def map_task_id_for_solution(predict_path, source_path):\n database = dict()\n raw_problems = Tools.load_tasks(source_path)\n for task_id in raw_problems.keys():\n database[raw_problems[task_id]['prompt']] = raw_problems[task_id]\n\n result = []\n predictions = Tools.load_jsonl(predict_path)\n for pre in predictions:\n task = database[pre['prompt']]\n if not pre['samples']:\n result.append({\n 'task_id': task['task_id'],\n 'prompt': pre['prompt'],\n 'test': task['test'],\n 'entry_point': task['entry_point'],\n 'completion': 'empty solution here, execution will fail'\n })\n for sample in pre['samples']:\n processed_code = PostProcessor.solution_extract(sample)\n result.append({\n 'task_id': task['task_id'],\n 'prompt': pre['prompt'],\n 'test': task['test'],\n 'entry_point': task['entry_point'],\n 'completion': processed_code\n })\n return result, len(raw_problems)\n\n @staticmethod\n def map_task_id_for_test_case(predict_path, source_path):\n database = dict()\n raw_problems = Tools.load_tasks(source_path)\n for task_id in raw_problems.keys():\n database[raw_problems[task_id]['prompt']] = raw_problems[task_id]\n\n test_cases_by_task = defaultdict(list)\n predictions = Tools.load_jsonl(predict_path)\n for pre in predictions:\n task = database[pre['prompt']]\n for sample in pre['samples']:\n test_cases = PostProcessor.test_case_extract(sample, task['entry_point'])\n test_cases_by_task[task['task_id']].append(test_cases)\n return test_cases_by_task\n\n @staticmethod\n def solution_extract(content):\n for identifier in STOP_TOKEN:","source_hash":"7ec392f825cb31702d88827a8fecba44178e785a13780d5f76db3332b1bd0331","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.postprocess.map_task_id_for_test_case","uri":"program://CodeT/function/CodeT.src.postprocess.map_task_id_for_test_case#L42-L55","kind":"function","name":"map_task_id_for_test_case","path":"CodeT/src/postprocess.py","language":"python","start_line":42,"end_line":55,"context_start_line":22,"context_end_line":75,"code":" if not pre['samples']:\n result.append({\n 'task_id': task['task_id'],\n 'prompt': pre['prompt'],\n 'test': task['test'],\n 'entry_point': task['entry_point'],\n 'completion': 'empty solution here, execution will fail'\n })\n for sample in pre['samples']:\n processed_code = PostProcessor.solution_extract(sample)\n result.append({\n 'task_id': task['task_id'],\n 'prompt': pre['prompt'],\n 'test': task['test'],\n 'entry_point': task['entry_point'],\n 'completion': processed_code\n })\n return result, len(raw_problems)\n\n @staticmethod\n def map_task_id_for_test_case(predict_path, source_path):\n database = dict()\n raw_problems = Tools.load_tasks(source_path)\n for task_id in raw_problems.keys():\n database[raw_problems[task_id]['prompt']] = raw_problems[task_id]\n\n test_cases_by_task = defaultdict(list)\n predictions = Tools.load_jsonl(predict_path)\n for pre in predictions:\n task = database[pre['prompt']]\n for sample in pre['samples']:\n test_cases = PostProcessor.test_case_extract(sample, task['entry_point'])\n test_cases_by_task[task['task_id']].append(test_cases)\n return test_cases_by_task\n\n @staticmethod\n def solution_extract(content):\n for identifier in STOP_TOKEN:\n if identifier in content:\n content = content.split(identifier)[0]\n return content\n \n @staticmethod\n def test_case_extract(content, entry_point):\n def _truncate(content):\n for identifier in STOP_TOKEN:\n if identifier in content:\n content = content.split(identifier)[0]\n return content.strip()\n \n split_by_assert = [f'assert {part}'.strip() for part in f'assert {content}'.split('assert ') if (entry_point.strip() in part) and len(part.strip()) > 0]\n truncated_test_cases = [_truncate(i) for i in split_by_assert]\n checked_assertions = [i for i in truncated_test_cases if PostProcessor._check_test_case_validation(i)]\n return checked_assertions","source_hash":"7ec392f825cb31702d88827a8fecba44178e785a13780d5f76db3332b1bd0331","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.postprocess.solution_extract","uri":"program://CodeT/function/CodeT.src.postprocess.solution_extract#L58-L62","kind":"function","name":"solution_extract","path":"CodeT/src/postprocess.py","language":"python","start_line":58,"end_line":62,"context_start_line":38,"context_end_line":82,"code":" })\n return result, len(raw_problems)\n\n @staticmethod\n def map_task_id_for_test_case(predict_path, source_path):\n database = dict()\n raw_problems = Tools.load_tasks(source_path)\n for task_id in raw_problems.keys():\n database[raw_problems[task_id]['prompt']] = raw_problems[task_id]\n\n test_cases_by_task = defaultdict(list)\n predictions = Tools.load_jsonl(predict_path)\n for pre in predictions:\n task = database[pre['prompt']]\n for sample in pre['samples']:\n test_cases = PostProcessor.test_case_extract(sample, task['entry_point'])\n test_cases_by_task[task['task_id']].append(test_cases)\n return test_cases_by_task\n\n @staticmethod\n def solution_extract(content):\n for identifier in STOP_TOKEN:\n if identifier in content:\n content = content.split(identifier)[0]\n return content\n \n @staticmethod\n def test_case_extract(content, entry_point):\n def _truncate(content):\n for identifier in STOP_TOKEN:\n if identifier in content:\n content = content.split(identifier)[0]\n return content.strip()\n \n split_by_assert = [f'assert {part}'.strip() for part in f'assert {content}'.split('assert ') if (entry_point.strip() in part) and len(part.strip()) > 0]\n truncated_test_cases = [_truncate(i) for i in split_by_assert]\n checked_assertions = [i for i in truncated_test_cases if PostProcessor._check_test_case_validation(i)]\n return checked_assertions\n\n @staticmethod\n def _check_test_case_validation(test_case):\n if len(test_case.strip()) < 1:\n return False\n if 'assert' not in test_case:\n return False","source_hash":"7ec392f825cb31702d88827a8fecba44178e785a13780d5f76db3332b1bd0331","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.postprocess.test_case_extract","uri":"program://CodeT/function/CodeT.src.postprocess.test_case_extract#L65-L75","kind":"function","name":"test_case_extract","path":"CodeT/src/postprocess.py","language":"python","start_line":65,"end_line":75,"context_start_line":45,"context_end_line":89,"code":" for task_id in raw_problems.keys():\n database[raw_problems[task_id]['prompt']] = raw_problems[task_id]\n\n test_cases_by_task = defaultdict(list)\n predictions = Tools.load_jsonl(predict_path)\n for pre in predictions:\n task = database[pre['prompt']]\n for sample in pre['samples']:\n test_cases = PostProcessor.test_case_extract(sample, task['entry_point'])\n test_cases_by_task[task['task_id']].append(test_cases)\n return test_cases_by_task\n\n @staticmethod\n def solution_extract(content):\n for identifier in STOP_TOKEN:\n if identifier in content:\n content = content.split(identifier)[0]\n return content\n \n @staticmethod\n def test_case_extract(content, entry_point):\n def _truncate(content):\n for identifier in STOP_TOKEN:\n if identifier in content:\n content = content.split(identifier)[0]\n return content.strip()\n \n split_by_assert = [f'assert {part}'.strip() for part in f'assert {content}'.split('assert ') if (entry_point.strip() in part) and len(part.strip()) > 0]\n truncated_test_cases = [_truncate(i) for i in split_by_assert]\n checked_assertions = [i for i in truncated_test_cases if PostProcessor._check_test_case_validation(i)]\n return checked_assertions\n\n @staticmethod\n def _check_test_case_validation(test_case):\n if len(test_case.strip()) < 1:\n return False\n if 'assert' not in test_case:\n return False\n try:\n multi_line_test_case = test_case.replace(\"\\n\", \"\\n \")\n assert_in_a_block = f'try:\\n {multi_line_test_case}\\nexcept:\\n pass\\n'\n compile(assert_in_a_block, '', 'exec')\n return True\n except Exception:\n return False","source_hash":"7ec392f825cb31702d88827a8fecba44178e785a13780d5f76db3332b1bd0331","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.postprocess._check_test_case_validation","uri":"program://CodeT/function/CodeT.src.postprocess._check_test_case_validation#L78-L89","kind":"function","name":"_check_test_case_validation","path":"CodeT/src/postprocess.py","language":"python","start_line":78,"end_line":89,"context_start_line":58,"context_end_line":89,"code":" def solution_extract(content):\n for identifier in STOP_TOKEN:\n if identifier in content:\n content = content.split(identifier)[0]\n return content\n \n @staticmethod\n def test_case_extract(content, entry_point):\n def _truncate(content):\n for identifier in STOP_TOKEN:\n if identifier in content:\n content = content.split(identifier)[0]\n return content.strip()\n \n split_by_assert = [f'assert {part}'.strip() for part in f'assert {content}'.split('assert ') if (entry_point.strip() in part) and len(part.strip()) > 0]\n truncated_test_cases = [_truncate(i) for i in split_by_assert]\n checked_assertions = [i for i in truncated_test_cases if PostProcessor._check_test_case_validation(i)]\n return checked_assertions\n\n @staticmethod\n def _check_test_case_validation(test_case):\n if len(test_case.strip()) < 1:\n return False\n if 'assert' not in test_case:\n return False\n try:\n multi_line_test_case = test_case.replace(\"\\n\", \"\\n \")\n assert_in_a_block = f'try:\\n {multi_line_test_case}\\nexcept:\\n pass\\n'\n compile(assert_in_a_block, '', 'exec')\n return True\n except Exception:\n return False","source_hash":"7ec392f825cb31702d88827a8fecba44178e785a13780d5f76db3332b1bd0331","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.postprocess._truncate","uri":"program://CodeT/function/CodeT.src.postprocess._truncate#L66-L70","kind":"function","name":"_truncate","path":"CodeT/src/postprocess.py","language":"python","start_line":66,"end_line":70,"context_start_line":46,"context_end_line":89,"code":" database[raw_problems[task_id]['prompt']] = raw_problems[task_id]\n\n test_cases_by_task = defaultdict(list)\n predictions = Tools.load_jsonl(predict_path)\n for pre in predictions:\n task = database[pre['prompt']]\n for sample in pre['samples']:\n test_cases = PostProcessor.test_case_extract(sample, task['entry_point'])\n test_cases_by_task[task['task_id']].append(test_cases)\n return test_cases_by_task\n\n @staticmethod\n def solution_extract(content):\n for identifier in STOP_TOKEN:\n if identifier in content:\n content = content.split(identifier)[0]\n return content\n \n @staticmethod\n def test_case_extract(content, entry_point):\n def _truncate(content):\n for identifier in STOP_TOKEN:\n if identifier in content:\n content = content.split(identifier)[0]\n return content.strip()\n \n split_by_assert = [f'assert {part}'.strip() for part in f'assert {content}'.split('assert ') if (entry_point.strip() in part) and len(part.strip()) > 0]\n truncated_test_cases = [_truncate(i) for i in split_by_assert]\n checked_assertions = [i for i in truncated_test_cases if PostProcessor._check_test_case_validation(i)]\n return checked_assertions\n\n @staticmethod\n def _check_test_case_validation(test_case):\n if len(test_case.strip()) < 1:\n return False\n if 'assert' not in test_case:\n return False\n try:\n multi_line_test_case = test_case.replace(\"\\n\", \"\\n \")\n assert_in_a_block = f'try:\\n {multi_line_test_case}\\nexcept:\\n pass\\n'\n compile(assert_in_a_block, '', 'exec')\n return True\n except Exception:\n return False","source_hash":"7ec392f825cb31702d88827a8fecba44178e785a13780d5f76db3332b1bd0331","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.evaluation","uri":"program://CodeT/module/CodeT.src.evaluation#L1-L141","kind":"module","name":"CodeT.src.evaluation","path":"CodeT/src/evaluation.py","language":"python","start_line":1,"end_line":141,"context_start_line":1,"context_end_line":141,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport statistics\nimport numpy as np\nfrom collections import defaultdict\nimport logging\nfrom typing import List, Union\nimport itertools\n\nlogging.basicConfig(\n format=\"SystemLog: [%(asctime)s][%(name)s][%(levelname)s] - %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n level=logging.INFO,\n)\n\nlogger = logging.getLogger(__name__)\n\ndef _dictionized_ground_truth_results(ground_truth_exec_results):\n ground_truth_results_by_task_and_solution = defaultdict(defaultdict)\n for result in ground_truth_exec_results:\n ground_truth_results_by_task_and_solution[result['task_id']][result['completion']] = result['passed']\n return ground_truth_results_by_task_and_solution\n\ndef _turn_solution_scores_into_choose_count(sorted_solution_scores, topk):\n # sorted_solution_scores: list of (solution, score)\n # if wrapped, sorted_solution_scores is list of ([solutions], score)\n # return list of (solution, choose_count)\n wrapped = True if type(sorted_solution_scores[0][0]) == list else False\n result = []\n if wrapped:\n last_score = sorted_solution_scores[0][1]\n merged_solutions_and_score = [sorted_solution_scores[0]]\n for solutions, score in sorted_solution_scores[1:]:\n if score == last_score:\n last_solutions = merged_solutions_and_score[-1][0]\n merged_solutions_and_score[-1] = (last_solutions + solutions, score)\n else:\n merged_solutions_and_score.append((solutions, score))\n last_score = score\n for solutions_and_score in merged_solutions_and_score:\n result.append((solutions_and_score[0], 1)) # choose one from solutions_and_score\n else:\n topk_scores = sorted(list(set([i[1] for i in sorted_solution_scores])), reverse=True)\n for score in topk_scores:\n solutions = [s[0] for s in sorted_solution_scores if s[1] == score]\n result.append((solutions, 1))\n\n if len(result) >= topk:\n return result[:topk]\n else:\n intial_choose_count = [1]*len(result)\n for i in range(topk-len(result)):\n intial_choose_count[i%len(result)] += 1\n for i, choose_count in enumerate(intial_choose_count):\n result[i] = (result[i][0], choose_count)\n return result\n \n\ndef get_result_of_sorted_solutions(ground_truth_results_list, sorted_solutions_by_task, topks=[1,2,10]):\n # sorted_solutions_by_task {task_id: [([solutions], score), ...]}\n def _count_correct(solutions: list, ground_truth_results: dict) -> int:\n return sum([ground_truth_results[s] for s in solutions])\n \n ground_truth_results = _dictionized_ground_truth_results(ground_truth_results_list)\n topk_results = dict()\n for topk in topks:\n random_pass_at_k_by_task = pass_at_K_by_task(ground_truth_results_list, k=topk)\n pass_rates = []\n for task_id in ground_truth_results.keys():\n all_wrong_probability = 1\n if task_id in sorted_solutions_by_task and sorted_solutions_by_task[task_id]:\n solutions_and_probability = _turn_solution_scores_into_choose_count(sorted_solutions_by_task[task_id], topk)\n for solutions, choose_count in solutions_and_probability:\n current_wrong_prob = _estimator(len(solutions), _count_correct(solutions, ground_truth_results[task_id]), 1)\n repeat_current_wrong_prob = pow(current_wrong_prob, choose_count)\n all_wrong_probability *= repeat_current_wrong_prob\n pass_rates.append(1-all_wrong_probability)\n else:\n pass_rates.append(random_pass_at_k_by_task[task_id])\n \n # the avg rate of all tasks\n topk_results[f'pass@{topk}'] = round(statistics.mean(pass_rates), 4)\n logger.info(topk_results)\n\ndef pass_at_K_by_task(results, k):\n result_dict = defaultdict(list)\n for line in results:\n result_dict[line['task_id']].append(line['passed'])\n result = dict()\n for task_id in result_dict.keys():\n total = len(result_dict[task_id])\n correct = sum(result_dict[task_id])\n score = _estimate_pass_at_k(total, [correct], k)[0]\n result[task_id] = score\n return result\n\ndef pass_at_K(results, k = [1, 10, 100]):\n def _turn_list_into_dict(result_lines):\n result_dict = defaultdict(list)\n for line in result_lines:\n result_dict[line['task_id']].append(line['passed'])\n return result_dict\n\n # Calculate pass@k.\n total, correct = [], []\n for passed in _turn_list_into_dict(results).values():\n total.append(len(passed))\n correct.append(sum(passed))\n\n total = np.array(total)\n correct = np.array(correct)\n\n ks = k\n pass_at_k = {f\"pass@{k}\": round(_estimate_pass_at_k(total, correct, k).mean(), 4)\n for k in ks if (total >= k).all()}\n logger.info(pass_at_k)\n\ndef _estimator(n: int, c: int, k: int) -> float:\n \"\"\"\n Calculates comb(n - c, k) / comb(n, k).\n \"\"\"\n if n - c < k:\n return 0\n return np.prod(1.0 - k / np.arange(n - c + 1, n + 1))\n\ndef _estimate_pass_at_k(\n num_samples: Union[int, List[int], np.ndarray],\n num_correct: Union[List[int], np.ndarray],\n k: int\n) -> np.ndarray:\n \"\"\"\n Estimates pass@k of each problem and returns them in an array.\n \"\"\"\n if isinstance(num_samples, int):\n num_samples_it = itertools.repeat(num_samples, len(num_correct))\n else:\n assert len(num_samples) == len(num_correct)\n num_samples_it = iter(num_samples)\n\n return np.array([1.0 - _estimator(int(n), int(c), k) for n, c in zip(num_samples_it, num_correct)])","source_hash":"25ce065e7452ea0a489d57a0ce8c049edcd383f98c21819462d05e222fe840c0","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.evaluation._dictionized_ground_truth_results","uri":"program://CodeT/function/CodeT.src.evaluation._dictionized_ground_truth_results#L19-L23","kind":"function","name":"_dictionized_ground_truth_results","path":"CodeT/src/evaluation.py","language":"python","start_line":19,"end_line":23,"context_start_line":1,"context_end_line":43,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport statistics\nimport numpy as np\nfrom collections import defaultdict\nimport logging\nfrom typing import List, Union\nimport itertools\n\nlogging.basicConfig(\n format=\"SystemLog: [%(asctime)s][%(name)s][%(levelname)s] - %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n level=logging.INFO,\n)\n\nlogger = logging.getLogger(__name__)\n\ndef _dictionized_ground_truth_results(ground_truth_exec_results):\n ground_truth_results_by_task_and_solution = defaultdict(defaultdict)\n for result in ground_truth_exec_results:\n ground_truth_results_by_task_and_solution[result['task_id']][result['completion']] = result['passed']\n return ground_truth_results_by_task_and_solution\n\ndef _turn_solution_scores_into_choose_count(sorted_solution_scores, topk):\n # sorted_solution_scores: list of (solution, score)\n # if wrapped, sorted_solution_scores is list of ([solutions], score)\n # return list of (solution, choose_count)\n wrapped = True if type(sorted_solution_scores[0][0]) == list else False\n result = []\n if wrapped:\n last_score = sorted_solution_scores[0][1]\n merged_solutions_and_score = [sorted_solution_scores[0]]\n for solutions, score in sorted_solution_scores[1:]:\n if score == last_score:\n last_solutions = merged_solutions_and_score[-1][0]\n merged_solutions_and_score[-1] = (last_solutions + solutions, score)\n else:\n merged_solutions_and_score.append((solutions, score))\n last_score = score\n for solutions_and_score in merged_solutions_and_score:\n result.append((solutions_and_score[0], 1)) # choose one from solutions_and_score\n else:","source_hash":"25ce065e7452ea0a489d57a0ce8c049edcd383f98c21819462d05e222fe840c0","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.evaluation._turn_solution_scores_into_choose_count","uri":"program://CodeT/function/CodeT.src.evaluation._turn_solution_scores_into_choose_count#L25-L57","kind":"function","name":"_turn_solution_scores_into_choose_count","path":"CodeT/src/evaluation.py","language":"python","start_line":25,"end_line":57,"context_start_line":5,"context_end_line":77,"code":"import numpy as np\nfrom collections import defaultdict\nimport logging\nfrom typing import List, Union\nimport itertools\n\nlogging.basicConfig(\n format=\"SystemLog: [%(asctime)s][%(name)s][%(levelname)s] - %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n level=logging.INFO,\n)\n\nlogger = logging.getLogger(__name__)\n\ndef _dictionized_ground_truth_results(ground_truth_exec_results):\n ground_truth_results_by_task_and_solution = defaultdict(defaultdict)\n for result in ground_truth_exec_results:\n ground_truth_results_by_task_and_solution[result['task_id']][result['completion']] = result['passed']\n return ground_truth_results_by_task_and_solution\n\ndef _turn_solution_scores_into_choose_count(sorted_solution_scores, topk):\n # sorted_solution_scores: list of (solution, score)\n # if wrapped, sorted_solution_scores is list of ([solutions], score)\n # return list of (solution, choose_count)\n wrapped = True if type(sorted_solution_scores[0][0]) == list else False\n result = []\n if wrapped:\n last_score = sorted_solution_scores[0][1]\n merged_solutions_and_score = [sorted_solution_scores[0]]\n for solutions, score in sorted_solution_scores[1:]:\n if score == last_score:\n last_solutions = merged_solutions_and_score[-1][0]\n merged_solutions_and_score[-1] = (last_solutions + solutions, score)\n else:\n merged_solutions_and_score.append((solutions, score))\n last_score = score\n for solutions_and_score in merged_solutions_and_score:\n result.append((solutions_and_score[0], 1)) # choose one from solutions_and_score\n else:\n topk_scores = sorted(list(set([i[1] for i in sorted_solution_scores])), reverse=True)\n for score in topk_scores:\n solutions = [s[0] for s in sorted_solution_scores if s[1] == score]\n result.append((solutions, 1))\n\n if len(result) >= topk:\n return result[:topk]\n else:\n intial_choose_count = [1]*len(result)\n for i in range(topk-len(result)):\n intial_choose_count[i%len(result)] += 1\n for i, choose_count in enumerate(intial_choose_count):\n result[i] = (result[i][0], choose_count)\n return result\n \n\ndef get_result_of_sorted_solutions(ground_truth_results_list, sorted_solutions_by_task, topks=[1,2,10]):\n # sorted_solutions_by_task {task_id: [([solutions], score), ...]}\n def _count_correct(solutions: list, ground_truth_results: dict) -> int:\n return sum([ground_truth_results[s] for s in solutions])\n \n ground_truth_results = _dictionized_ground_truth_results(ground_truth_results_list)\n topk_results = dict()\n for topk in topks:\n random_pass_at_k_by_task = pass_at_K_by_task(ground_truth_results_list, k=topk)\n pass_rates = []\n for task_id in ground_truth_results.keys():\n all_wrong_probability = 1\n if task_id in sorted_solutions_by_task and sorted_solutions_by_task[task_id]:\n solutions_and_probability = _turn_solution_scores_into_choose_count(sorted_solutions_by_task[task_id], topk)\n for solutions, choose_count in solutions_and_probability:\n current_wrong_prob = _estimator(len(solutions), _count_correct(solutions, ground_truth_results[task_id]), 1)\n repeat_current_wrong_prob = pow(current_wrong_prob, choose_count)\n all_wrong_probability *= repeat_current_wrong_prob","source_hash":"25ce065e7452ea0a489d57a0ce8c049edcd383f98c21819462d05e222fe840c0","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.evaluation.get_result_of_sorted_solutions","uri":"program://CodeT/function/CodeT.src.evaluation.get_result_of_sorted_solutions#L60-L84","kind":"function","name":"get_result_of_sorted_solutions","path":"CodeT/src/evaluation.py","language":"python","start_line":60,"end_line":84,"context_start_line":40,"context_end_line":104,"code":" last_score = score\n for solutions_and_score in merged_solutions_and_score:\n result.append((solutions_and_score[0], 1)) # choose one from solutions_and_score\n else:\n topk_scores = sorted(list(set([i[1] for i in sorted_solution_scores])), reverse=True)\n for score in topk_scores:\n solutions = [s[0] for s in sorted_solution_scores if s[1] == score]\n result.append((solutions, 1))\n\n if len(result) >= topk:\n return result[:topk]\n else:\n intial_choose_count = [1]*len(result)\n for i in range(topk-len(result)):\n intial_choose_count[i%len(result)] += 1\n for i, choose_count in enumerate(intial_choose_count):\n result[i] = (result[i][0], choose_count)\n return result\n \n\ndef get_result_of_sorted_solutions(ground_truth_results_list, sorted_solutions_by_task, topks=[1,2,10]):\n # sorted_solutions_by_task {task_id: [([solutions], score), ...]}\n def _count_correct(solutions: list, ground_truth_results: dict) -> int:\n return sum([ground_truth_results[s] for s in solutions])\n \n ground_truth_results = _dictionized_ground_truth_results(ground_truth_results_list)\n topk_results = dict()\n for topk in topks:\n random_pass_at_k_by_task = pass_at_K_by_task(ground_truth_results_list, k=topk)\n pass_rates = []\n for task_id in ground_truth_results.keys():\n all_wrong_probability = 1\n if task_id in sorted_solutions_by_task and sorted_solutions_by_task[task_id]:\n solutions_and_probability = _turn_solution_scores_into_choose_count(sorted_solutions_by_task[task_id], topk)\n for solutions, choose_count in solutions_and_probability:\n current_wrong_prob = _estimator(len(solutions), _count_correct(solutions, ground_truth_results[task_id]), 1)\n repeat_current_wrong_prob = pow(current_wrong_prob, choose_count)\n all_wrong_probability *= repeat_current_wrong_prob\n pass_rates.append(1-all_wrong_probability)\n else:\n pass_rates.append(random_pass_at_k_by_task[task_id])\n \n # the avg rate of all tasks\n topk_results[f'pass@{topk}'] = round(statistics.mean(pass_rates), 4)\n logger.info(topk_results)\n\ndef pass_at_K_by_task(results, k):\n result_dict = defaultdict(list)\n for line in results:\n result_dict[line['task_id']].append(line['passed'])\n result = dict()\n for task_id in result_dict.keys():\n total = len(result_dict[task_id])\n correct = sum(result_dict[task_id])\n score = _estimate_pass_at_k(total, [correct], k)[0]\n result[task_id] = score\n return result\n\ndef pass_at_K(results, k = [1, 10, 100]):\n def _turn_list_into_dict(result_lines):\n result_dict = defaultdict(list)\n for line in result_lines:\n result_dict[line['task_id']].append(line['passed'])\n return result_dict\n","source_hash":"25ce065e7452ea0a489d57a0ce8c049edcd383f98c21819462d05e222fe840c0","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.evaluation.pass_at_K_by_task","uri":"program://CodeT/function/CodeT.src.evaluation.pass_at_K_by_task#L86-L96","kind":"function","name":"pass_at_K_by_task","path":"CodeT/src/evaluation.py","language":"python","start_line":86,"end_line":96,"context_start_line":66,"context_end_line":116,"code":" topk_results = dict()\n for topk in topks:\n random_pass_at_k_by_task = pass_at_K_by_task(ground_truth_results_list, k=topk)\n pass_rates = []\n for task_id in ground_truth_results.keys():\n all_wrong_probability = 1\n if task_id in sorted_solutions_by_task and sorted_solutions_by_task[task_id]:\n solutions_and_probability = _turn_solution_scores_into_choose_count(sorted_solutions_by_task[task_id], topk)\n for solutions, choose_count in solutions_and_probability:\n current_wrong_prob = _estimator(len(solutions), _count_correct(solutions, ground_truth_results[task_id]), 1)\n repeat_current_wrong_prob = pow(current_wrong_prob, choose_count)\n all_wrong_probability *= repeat_current_wrong_prob\n pass_rates.append(1-all_wrong_probability)\n else:\n pass_rates.append(random_pass_at_k_by_task[task_id])\n \n # the avg rate of all tasks\n topk_results[f'pass@{topk}'] = round(statistics.mean(pass_rates), 4)\n logger.info(topk_results)\n\ndef pass_at_K_by_task(results, k):\n result_dict = defaultdict(list)\n for line in results:\n result_dict[line['task_id']].append(line['passed'])\n result = dict()\n for task_id in result_dict.keys():\n total = len(result_dict[task_id])\n correct = sum(result_dict[task_id])\n score = _estimate_pass_at_k(total, [correct], k)[0]\n result[task_id] = score\n return result\n\ndef pass_at_K(results, k = [1, 10, 100]):\n def _turn_list_into_dict(result_lines):\n result_dict = defaultdict(list)\n for line in result_lines:\n result_dict[line['task_id']].append(line['passed'])\n return result_dict\n\n # Calculate pass@k.\n total, correct = [], []\n for passed in _turn_list_into_dict(results).values():\n total.append(len(passed))\n correct.append(sum(passed))\n\n total = np.array(total)\n correct = np.array(correct)\n\n ks = k\n pass_at_k = {f\"pass@{k}\": round(_estimate_pass_at_k(total, correct, k).mean(), 4)\n for k in ks if (total >= k).all()}","source_hash":"25ce065e7452ea0a489d57a0ce8c049edcd383f98c21819462d05e222fe840c0","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.evaluation.pass_at_K","uri":"program://CodeT/function/CodeT.src.evaluation.pass_at_K#L98-L117","kind":"function","name":"pass_at_K","path":"CodeT/src/evaluation.py","language":"python","start_line":98,"end_line":117,"context_start_line":78,"context_end_line":137,"code":" pass_rates.append(1-all_wrong_probability)\n else:\n pass_rates.append(random_pass_at_k_by_task[task_id])\n \n # the avg rate of all tasks\n topk_results[f'pass@{topk}'] = round(statistics.mean(pass_rates), 4)\n logger.info(topk_results)\n\ndef pass_at_K_by_task(results, k):\n result_dict = defaultdict(list)\n for line in results:\n result_dict[line['task_id']].append(line['passed'])\n result = dict()\n for task_id in result_dict.keys():\n total = len(result_dict[task_id])\n correct = sum(result_dict[task_id])\n score = _estimate_pass_at_k(total, [correct], k)[0]\n result[task_id] = score\n return result\n\ndef pass_at_K(results, k = [1, 10, 100]):\n def _turn_list_into_dict(result_lines):\n result_dict = defaultdict(list)\n for line in result_lines:\n result_dict[line['task_id']].append(line['passed'])\n return result_dict\n\n # Calculate pass@k.\n total, correct = [], []\n for passed in _turn_list_into_dict(results).values():\n total.append(len(passed))\n correct.append(sum(passed))\n\n total = np.array(total)\n correct = np.array(correct)\n\n ks = k\n pass_at_k = {f\"pass@{k}\": round(_estimate_pass_at_k(total, correct, k).mean(), 4)\n for k in ks if (total >= k).all()}\n logger.info(pass_at_k)\n\ndef _estimator(n: int, c: int, k: int) -> float:\n \"\"\"\n Calculates comb(n - c, k) / comb(n, k).\n \"\"\"\n if n - c < k:\n return 0\n return np.prod(1.0 - k / np.arange(n - c + 1, n + 1))\n\ndef _estimate_pass_at_k(\n num_samples: Union[int, List[int], np.ndarray],\n num_correct: Union[List[int], np.ndarray],\n k: int\n) -> np.ndarray:\n \"\"\"\n Estimates pass@k of each problem and returns them in an array.\n \"\"\"\n if isinstance(num_samples, int):\n num_samples_it = itertools.repeat(num_samples, len(num_correct))\n else:","source_hash":"25ce065e7452ea0a489d57a0ce8c049edcd383f98c21819462d05e222fe840c0","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.evaluation._estimator","uri":"program://CodeT/function/CodeT.src.evaluation._estimator#L119-L125","kind":"function","name":"_estimator","path":"CodeT/src/evaluation.py","language":"python","start_line":119,"end_line":125,"context_start_line":99,"context_end_line":141,"code":" def _turn_list_into_dict(result_lines):\n result_dict = defaultdict(list)\n for line in result_lines:\n result_dict[line['task_id']].append(line['passed'])\n return result_dict\n\n # Calculate pass@k.\n total, correct = [], []\n for passed in _turn_list_into_dict(results).values():\n total.append(len(passed))\n correct.append(sum(passed))\n\n total = np.array(total)\n correct = np.array(correct)\n\n ks = k\n pass_at_k = {f\"pass@{k}\": round(_estimate_pass_at_k(total, correct, k).mean(), 4)\n for k in ks if (total >= k).all()}\n logger.info(pass_at_k)\n\ndef _estimator(n: int, c: int, k: int) -> float:\n \"\"\"\n Calculates comb(n - c, k) / comb(n, k).\n \"\"\"\n if n - c < k:\n return 0\n return np.prod(1.0 - k / np.arange(n - c + 1, n + 1))\n\ndef _estimate_pass_at_k(\n num_samples: Union[int, List[int], np.ndarray],\n num_correct: Union[List[int], np.ndarray],\n k: int\n) -> np.ndarray:\n \"\"\"\n Estimates pass@k of each problem and returns them in an array.\n \"\"\"\n if isinstance(num_samples, int):\n num_samples_it = itertools.repeat(num_samples, len(num_correct))\n else:\n assert len(num_samples) == len(num_correct)\n num_samples_it = iter(num_samples)\n\n return np.array([1.0 - _estimator(int(n), int(c), k) for n, c in zip(num_samples_it, num_correct)])","source_hash":"25ce065e7452ea0a489d57a0ce8c049edcd383f98c21819462d05e222fe840c0","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.evaluation._estimate_pass_at_k","uri":"program://CodeT/function/CodeT.src.evaluation._estimate_pass_at_k#L127-L141","kind":"function","name":"_estimate_pass_at_k","path":"CodeT/src/evaluation.py","language":"python","start_line":127,"end_line":141,"context_start_line":107,"context_end_line":141,"code":" for passed in _turn_list_into_dict(results).values():\n total.append(len(passed))\n correct.append(sum(passed))\n\n total = np.array(total)\n correct = np.array(correct)\n\n ks = k\n pass_at_k = {f\"pass@{k}\": round(_estimate_pass_at_k(total, correct, k).mean(), 4)\n for k in ks if (total >= k).all()}\n logger.info(pass_at_k)\n\ndef _estimator(n: int, c: int, k: int) -> float:\n \"\"\"\n Calculates comb(n - c, k) / comb(n, k).\n \"\"\"\n if n - c < k:\n return 0\n return np.prod(1.0 - k / np.arange(n - c + 1, n + 1))\n\ndef _estimate_pass_at_k(\n num_samples: Union[int, List[int], np.ndarray],\n num_correct: Union[List[int], np.ndarray],\n k: int\n) -> np.ndarray:\n \"\"\"\n Estimates pass@k of each problem and returns them in an array.\n \"\"\"\n if isinstance(num_samples, int):\n num_samples_it = itertools.repeat(num_samples, len(num_correct))\n else:\n assert len(num_samples) == len(num_correct)\n num_samples_it = iter(num_samples)\n\n return np.array([1.0 - _estimator(int(n), int(c), k) for n, c in zip(num_samples_it, num_correct)])","source_hash":"25ce065e7452ea0a489d57a0ce8c049edcd383f98c21819462d05e222fe840c0","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.evaluation._count_correct","uri":"program://CodeT/function/CodeT.src.evaluation._count_correct#L62-L63","kind":"function","name":"_count_correct","path":"CodeT/src/evaluation.py","language":"python","start_line":62,"end_line":63,"context_start_line":42,"context_end_line":83,"code":" result.append((solutions_and_score[0], 1)) # choose one from solutions_and_score\n else:\n topk_scores = sorted(list(set([i[1] for i in sorted_solution_scores])), reverse=True)\n for score in topk_scores:\n solutions = [s[0] for s in sorted_solution_scores if s[1] == score]\n result.append((solutions, 1))\n\n if len(result) >= topk:\n return result[:topk]\n else:\n intial_choose_count = [1]*len(result)\n for i in range(topk-len(result)):\n intial_choose_count[i%len(result)] += 1\n for i, choose_count in enumerate(intial_choose_count):\n result[i] = (result[i][0], choose_count)\n return result\n \n\ndef get_result_of_sorted_solutions(ground_truth_results_list, sorted_solutions_by_task, topks=[1,2,10]):\n # sorted_solutions_by_task {task_id: [([solutions], score), ...]}\n def _count_correct(solutions: list, ground_truth_results: dict) -> int:\n return sum([ground_truth_results[s] for s in solutions])\n \n ground_truth_results = _dictionized_ground_truth_results(ground_truth_results_list)\n topk_results = dict()\n for topk in topks:\n random_pass_at_k_by_task = pass_at_K_by_task(ground_truth_results_list, k=topk)\n pass_rates = []\n for task_id in ground_truth_results.keys():\n all_wrong_probability = 1\n if task_id in sorted_solutions_by_task and sorted_solutions_by_task[task_id]:\n solutions_and_probability = _turn_solution_scores_into_choose_count(sorted_solutions_by_task[task_id], topk)\n for solutions, choose_count in solutions_and_probability:\n current_wrong_prob = _estimator(len(solutions), _count_correct(solutions, ground_truth_results[task_id]), 1)\n repeat_current_wrong_prob = pow(current_wrong_prob, choose_count)\n all_wrong_probability *= repeat_current_wrong_prob\n pass_rates.append(1-all_wrong_probability)\n else:\n pass_rates.append(random_pass_at_k_by_task[task_id])\n \n # the avg rate of all tasks\n topk_results[f'pass@{topk}'] = round(statistics.mean(pass_rates), 4)","source_hash":"25ce065e7452ea0a489d57a0ce8c049edcd383f98c21819462d05e222fe840c0","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.evaluation._turn_list_into_dict","uri":"program://CodeT/function/CodeT.src.evaluation._turn_list_into_dict#L99-L103","kind":"function","name":"_turn_list_into_dict","path":"CodeT/src/evaluation.py","language":"python","start_line":99,"end_line":103,"context_start_line":79,"context_end_line":123,"code":" else:\n pass_rates.append(random_pass_at_k_by_task[task_id])\n \n # the avg rate of all tasks\n topk_results[f'pass@{topk}'] = round(statistics.mean(pass_rates), 4)\n logger.info(topk_results)\n\ndef pass_at_K_by_task(results, k):\n result_dict = defaultdict(list)\n for line in results:\n result_dict[line['task_id']].append(line['passed'])\n result = dict()\n for task_id in result_dict.keys():\n total = len(result_dict[task_id])\n correct = sum(result_dict[task_id])\n score = _estimate_pass_at_k(total, [correct], k)[0]\n result[task_id] = score\n return result\n\ndef pass_at_K(results, k = [1, 10, 100]):\n def _turn_list_into_dict(result_lines):\n result_dict = defaultdict(list)\n for line in result_lines:\n result_dict[line['task_id']].append(line['passed'])\n return result_dict\n\n # Calculate pass@k.\n total, correct = [], []\n for passed in _turn_list_into_dict(results).values():\n total.append(len(passed))\n correct.append(sum(passed))\n\n total = np.array(total)\n correct = np.array(correct)\n\n ks = k\n pass_at_k = {f\"pass@{k}\": round(_estimate_pass_at_k(total, correct, k).mean(), 4)\n for k in ks if (total >= k).all()}\n logger.info(pass_at_k)\n\ndef _estimator(n: int, c: int, k: int) -> float:\n \"\"\"\n Calculates comb(n - c, k) / comb(n, k).\n \"\"\"\n if n - c < k:","source_hash":"25ce065e7452ea0a489d57a0ce8c049edcd383f98c21819462d05e222fe840c0","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution","uri":"program://CodeT/module/CodeT.src._execution#L1-L290","kind":"module","name":"CodeT.src._execution","path":"CodeT/src/_execution.py","language":"python","start_line":1,"end_line":290,"context_start_line":1,"context_end_line":290,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom typing import Optional, Dict\nimport contextlib\nimport faulthandler\nimport io\nimport os\nimport multiprocessing\nimport platform\nimport signal\nimport tempfile\n\ndef _pack_test_cases(test_cases, timeout):\n blank_4 = ' ' * 4\n blank_8 = ' ' * 8\n blank_12 = ' ' * 12\n result = f'def check():\\n pass_result = []\\n'\n for idx, tc in enumerate(test_cases):\n multi_line_assertion = tc.strip().replace('\\n', f'\\n{blank_12}')\n result += f'\\n{blank_4}try:\\n{blank_8}with time_limit({timeout}):\\n{blank_12}{multi_line_assertion}\\\n \\n{blank_12}pass_result.append(True)\\n{blank_4}except Exception as e:\\n{blank_8}pass_result.append(False)\\n'\n result += '\\n return pass_result\\n'\n result += f'\\nglobal final_result\\nfinal_result = check()'\n return result\n\n\ndef check_correctness_with_test_cases(task_id, prompt, completion, test_cases, timeout):\n \"\"\"\n Evaluates the functional correctness of a solution_content by running the test\n suite provided in the problem. \n \"\"\"\n extend_timeout = timeout*len(test_cases)\n\n def unsafe_execute():\n\n with create_tempdir():\n\n # These system calls are needed when cleaning up tempdir.\n import os\n import shutil\n rmtree = shutil.rmtree\n rmdir = os.rmdir\n chdir = os.chdir\n\n # Disable functionalities that can make destructive changes to the test.\n reliability_guard()\n\n # Construct the check program and run it.\n check_program = (\n prompt + completion + \"\\n\" +\n _pack_test_cases(test_cases, timeout)\n )\n\n try:\n exec_globals = {'time_limit': time_limit}\n with swallow_io():\n exec(check_program, exec_globals)\n result.append(exec_globals['final_result'])\n except TimeoutException:\n result.append(\"timed out\")\n except BaseException as e:\n result.append(f\"failed: {e}\")\n\n # Needed for cleaning up.\n shutil.rmtree = rmtree\n os.rmdir = rmdir\n os.chdir = chdir\n\n manager = multiprocessing.Manager()\n result = manager.list()\n\n p = multiprocessing.Process(target=unsafe_execute)\n p.start()\n p.join(timeout=extend_timeout + 0.1)\n if p.is_alive():\n p.kill()\n\n if not result:\n result.append(\"timed out\")\n\n return dict(\n task_id=task_id,\n test_cases=test_cases,\n completion=completion,\n passed=(type(result[0]) == list) and len(result[0]) > 0,\n result=result[0]\n )\n\ndef check_correctness(task_id: str, prompt: str, completion: str, test: str, entry_point: str, timeout: float) -> Dict:\n \"\"\"\n Evaluates the functional correctness of a completion by running the test\n suite provided in the problem. \n \"\"\"\n\n def unsafe_execute():\n\n with create_tempdir():\n\n # These system calls are needed when cleaning up tempdir.\n import os\n import shutil\n rmtree = shutil.rmtree\n rmdir = os.rmdir\n chdir = os.chdir\n\n # Disable functionalities that can make destructive changes to the test.\n reliability_guard()\n\n # Construct the check program and run it.\n check_program = (\n prompt + completion + \"\\n\" + test + \"\\n\" + f'check({entry_point})'\n )\n\n try:\n exec_globals = {}\n with swallow_io():\n with time_limit(timeout):\n exec(check_program, exec_globals)\n result.append(\"passed\")\n except TimeoutException:\n result.append(\"timed out\")\n except BaseException as e:\n result.append(f\"failed: {e}\")\n\n # Needed for cleaning up.\n shutil.rmtree = rmtree\n os.rmdir = rmdir\n os.chdir = chdir\n\n manager = multiprocessing.Manager()\n result = manager.list()\n\n p = multiprocessing.Process(target=unsafe_execute)\n p.start()\n p.join(timeout=timeout+1)\n if p.is_alive():\n p.kill()\n\n if not result:\n result.append(\"timed out\")\n\n return dict(\n task_id=task_id,\n passed=result[0] == \"passed\",\n result=result[0],\n completion=completion,\n )\n\n@contextlib.contextmanager\ndef time_limit(seconds: float):\n def signal_handler(signum, frame):\n raise TimeoutException(\"Timed out!\")\n signal.setitimer(signal.ITIMER_REAL, seconds)\n signal.signal(signal.SIGALRM, signal_handler)\n try:\n yield\n finally:\n signal.setitimer(signal.ITIMER_REAL, 0)\n\n\n@contextlib.contextmanager\ndef swallow_io():\n stream = WriteOnlyStringIO()\n with contextlib.redirect_stdout(stream):\n with contextlib.redirect_stderr(stream):\n with redirect_stdin(stream):\n yield\n\n\n@contextlib.contextmanager\ndef create_tempdir():\n with tempfile.TemporaryDirectory() as dirname:\n with chdir(dirname):\n yield dirname\n\n\nclass TimeoutException(Exception):\n pass\n\n\nclass WriteOnlyStringIO(io.StringIO):\n \"\"\" StringIO that throws an exception when it's read from \"\"\"\n\n def read(self, *args, **kwargs):\n raise IOError\n\n def readline(self, *args, **kwargs):\n raise IOError\n\n def readlines(self, *args, **kwargs):\n raise IOError\n\n def readable(self, *args, **kwargs):\n \"\"\" Returns True if the IO object can be read. \"\"\"\n return False\n\n\nclass redirect_stdin(contextlib._RedirectStream): # type: ignore\n _stream = 'stdin'\n\n\n@contextlib.contextmanager\ndef chdir(root):\n if root == \".\":\n yield\n return\n cwd = os.getcwd()\n os.chdir(root)\n try:\n yield\n except BaseException as exc:\n raise exc\n finally:\n os.chdir(cwd)\n\n\ndef reliability_guard(maximum_memory_bytes: Optional[int] = None):\n \"\"\"\n This disables various destructive functions and prevents the generated code\n from interfering with the test (e.g. fork bomb, killing other processes,\n removing filesystem files, etc.)\n\n WARNING\n This function is NOT a security sandbox. Untrusted code, including, model-\n generated code, should not be blindly executed outside of one. See the \n Codex paper for more information about OpenAI's code sandbox, and proceed\n with caution.\n \"\"\"\n\n if maximum_memory_bytes is not None:\n import resource\n resource.setrlimit(resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes))\n resource.setrlimit(resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes))\n if not platform.uname().system == 'Darwin':\n resource.setrlimit(resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes))\n\n faulthandler.disable()\n\n import builtins\n builtins.exit = None\n builtins.quit = None\n\n import os\n os.environ['OMP_NUM_THREADS'] = '1'\n\n os.kill = None\n os.system = None\n os.putenv = None\n os.remove = None\n os.removedirs = None\n os.rmdir = None\n os.fchdir = None\n os.setuid = None\n os.fork = None\n os.forkpty = None\n os.killpg = None\n os.rename = None\n os.renames = None\n os.truncate = None\n os.replace = None\n os.unlink = None\n os.fchmod = None\n os.fchown = None\n os.chmod = None\n os.chown = None\n os.chroot = None\n os.fchdir = None\n os.lchflags = None\n os.lchmod = None\n os.lchown = None\n os.getcwd = None\n os.chdir = None\n\n import shutil\n shutil.rmtree = None\n shutil.move = None\n shutil.chown = None\n\n import subprocess\n subprocess.Popen = None # type: ignore\n\n __builtins__['help'] = None\n\n import sys\n sys.modules['ipdb'] = None\n sys.modules['joblib'] = None\n sys.modules['resource'] = None\n sys.modules['psutil'] = None\n sys.modules['tkinter'] = None","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution._pack_test_cases","uri":"program://CodeT/function/CodeT.src._execution._pack_test_cases#L14-L25","kind":"function","name":"_pack_test_cases","path":"CodeT/src/_execution.py","language":"python","start_line":14,"end_line":25,"context_start_line":1,"context_end_line":45,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom typing import Optional, Dict\nimport contextlib\nimport faulthandler\nimport io\nimport os\nimport multiprocessing\nimport platform\nimport signal\nimport tempfile\n\ndef _pack_test_cases(test_cases, timeout):\n blank_4 = ' ' * 4\n blank_8 = ' ' * 8\n blank_12 = ' ' * 12\n result = f'def check():\\n pass_result = []\\n'\n for idx, tc in enumerate(test_cases):\n multi_line_assertion = tc.strip().replace('\\n', f'\\n{blank_12}')\n result += f'\\n{blank_4}try:\\n{blank_8}with time_limit({timeout}):\\n{blank_12}{multi_line_assertion}\\\n \\n{blank_12}pass_result.append(True)\\n{blank_4}except Exception as e:\\n{blank_8}pass_result.append(False)\\n'\n result += '\\n return pass_result\\n'\n result += f'\\nglobal final_result\\nfinal_result = check()'\n return result\n\n\ndef check_correctness_with_test_cases(task_id, prompt, completion, test_cases, timeout):\n \"\"\"\n Evaluates the functional correctness of a solution_content by running the test\n suite provided in the problem. \n \"\"\"\n extend_timeout = timeout*len(test_cases)\n\n def unsafe_execute():\n\n with create_tempdir():\n\n # These system calls are needed when cleaning up tempdir.\n import os\n import shutil\n rmtree = shutil.rmtree\n rmdir = os.rmdir\n chdir = os.chdir\n","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.check_correctness_with_test_cases","uri":"program://CodeT/function/CodeT.src._execution.check_correctness_with_test_cases#L28-L88","kind":"function","name":"check_correctness_with_test_cases","path":"CodeT/src/_execution.py","language":"python","start_line":28,"end_line":88,"context_start_line":8,"context_end_line":108,"code":"import os\nimport multiprocessing\nimport platform\nimport signal\nimport tempfile\n\ndef _pack_test_cases(test_cases, timeout):\n blank_4 = ' ' * 4\n blank_8 = ' ' * 8\n blank_12 = ' ' * 12\n result = f'def check():\\n pass_result = []\\n'\n for idx, tc in enumerate(test_cases):\n multi_line_assertion = tc.strip().replace('\\n', f'\\n{blank_12}')\n result += f'\\n{blank_4}try:\\n{blank_8}with time_limit({timeout}):\\n{blank_12}{multi_line_assertion}\\\n \\n{blank_12}pass_result.append(True)\\n{blank_4}except Exception as e:\\n{blank_8}pass_result.append(False)\\n'\n result += '\\n return pass_result\\n'\n result += f'\\nglobal final_result\\nfinal_result = check()'\n return result\n\n\ndef check_correctness_with_test_cases(task_id, prompt, completion, test_cases, timeout):\n \"\"\"\n Evaluates the functional correctness of a solution_content by running the test\n suite provided in the problem. \n \"\"\"\n extend_timeout = timeout*len(test_cases)\n\n def unsafe_execute():\n\n with create_tempdir():\n\n # These system calls are needed when cleaning up tempdir.\n import os\n import shutil\n rmtree = shutil.rmtree\n rmdir = os.rmdir\n chdir = os.chdir\n\n # Disable functionalities that can make destructive changes to the test.\n reliability_guard()\n\n # Construct the check program and run it.\n check_program = (\n prompt + completion + \"\\n\" +\n _pack_test_cases(test_cases, timeout)\n )\n\n try:\n exec_globals = {'time_limit': time_limit}\n with swallow_io():\n exec(check_program, exec_globals)\n result.append(exec_globals['final_result'])\n except TimeoutException:\n result.append(\"timed out\")\n except BaseException as e:\n result.append(f\"failed: {e}\")\n\n # Needed for cleaning up.\n shutil.rmtree = rmtree\n os.rmdir = rmdir\n os.chdir = chdir\n\n manager = multiprocessing.Manager()\n result = manager.list()\n\n p = multiprocessing.Process(target=unsafe_execute)\n p.start()\n p.join(timeout=extend_timeout + 0.1)\n if p.is_alive():\n p.kill()\n\n if not result:\n result.append(\"timed out\")\n\n return dict(\n task_id=task_id,\n test_cases=test_cases,\n completion=completion,\n passed=(type(result[0]) == list) and len(result[0]) > 0,\n result=result[0]\n )\n\ndef check_correctness(task_id: str, prompt: str, completion: str, test: str, entry_point: str, timeout: float) -> Dict:\n \"\"\"\n Evaluates the functional correctness of a completion by running the test\n suite provided in the problem. \n \"\"\"\n\n def unsafe_execute():\n\n with create_tempdir():\n\n # These system calls are needed when cleaning up tempdir.\n import os\n import shutil\n rmtree = shutil.rmtree\n rmdir = os.rmdir\n chdir = os.chdir\n\n # Disable functionalities that can make destructive changes to the test.\n reliability_guard()","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.check_correctness","uri":"program://CodeT/function/CodeT.src._execution.check_correctness#L90-L148","kind":"function","name":"check_correctness","path":"CodeT/src/_execution.py","language":"python","start_line":90,"end_line":148,"context_start_line":70,"context_end_line":168,"code":" manager = multiprocessing.Manager()\n result = manager.list()\n\n p = multiprocessing.Process(target=unsafe_execute)\n p.start()\n p.join(timeout=extend_timeout + 0.1)\n if p.is_alive():\n p.kill()\n\n if not result:\n result.append(\"timed out\")\n\n return dict(\n task_id=task_id,\n test_cases=test_cases,\n completion=completion,\n passed=(type(result[0]) == list) and len(result[0]) > 0,\n result=result[0]\n )\n\ndef check_correctness(task_id: str, prompt: str, completion: str, test: str, entry_point: str, timeout: float) -> Dict:\n \"\"\"\n Evaluates the functional correctness of a completion by running the test\n suite provided in the problem. \n \"\"\"\n\n def unsafe_execute():\n\n with create_tempdir():\n\n # These system calls are needed when cleaning up tempdir.\n import os\n import shutil\n rmtree = shutil.rmtree\n rmdir = os.rmdir\n chdir = os.chdir\n\n # Disable functionalities that can make destructive changes to the test.\n reliability_guard()\n\n # Construct the check program and run it.\n check_program = (\n prompt + completion + \"\\n\" + test + \"\\n\" + f'check({entry_point})'\n )\n\n try:\n exec_globals = {}\n with swallow_io():\n with time_limit(timeout):\n exec(check_program, exec_globals)\n result.append(\"passed\")\n except TimeoutException:\n result.append(\"timed out\")\n except BaseException as e:\n result.append(f\"failed: {e}\")\n\n # Needed for cleaning up.\n shutil.rmtree = rmtree\n os.rmdir = rmdir\n os.chdir = chdir\n\n manager = multiprocessing.Manager()\n result = manager.list()\n\n p = multiprocessing.Process(target=unsafe_execute)\n p.start()\n p.join(timeout=timeout+1)\n if p.is_alive():\n p.kill()\n\n if not result:\n result.append(\"timed out\")\n\n return dict(\n task_id=task_id,\n passed=result[0] == \"passed\",\n result=result[0],\n completion=completion,\n )\n\n@contextlib.contextmanager\ndef time_limit(seconds: float):\n def signal_handler(signum, frame):\n raise TimeoutException(\"Timed out!\")\n signal.setitimer(signal.ITIMER_REAL, seconds)\n signal.signal(signal.SIGALRM, signal_handler)\n try:\n yield\n finally:\n signal.setitimer(signal.ITIMER_REAL, 0)\n\n\n@contextlib.contextmanager\ndef swallow_io():\n stream = WriteOnlyStringIO()\n with contextlib.redirect_stdout(stream):\n with contextlib.redirect_stderr(stream):\n with redirect_stdin(stream):\n yield","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.time_limit","uri":"program://CodeT/function/CodeT.src._execution.time_limit#L151-L159","kind":"function","name":"time_limit","path":"CodeT/src/_execution.py","language":"python","start_line":151,"end_line":159,"context_start_line":131,"context_end_line":179,"code":" manager = multiprocessing.Manager()\n result = manager.list()\n\n p = multiprocessing.Process(target=unsafe_execute)\n p.start()\n p.join(timeout=timeout+1)\n if p.is_alive():\n p.kill()\n\n if not result:\n result.append(\"timed out\")\n\n return dict(\n task_id=task_id,\n passed=result[0] == \"passed\",\n result=result[0],\n completion=completion,\n )\n\n@contextlib.contextmanager\ndef time_limit(seconds: float):\n def signal_handler(signum, frame):\n raise TimeoutException(\"Timed out!\")\n signal.setitimer(signal.ITIMER_REAL, seconds)\n signal.signal(signal.SIGALRM, signal_handler)\n try:\n yield\n finally:\n signal.setitimer(signal.ITIMER_REAL, 0)\n\n\n@contextlib.contextmanager\ndef swallow_io():\n stream = WriteOnlyStringIO()\n with contextlib.redirect_stdout(stream):\n with contextlib.redirect_stderr(stream):\n with redirect_stdin(stream):\n yield\n\n\n@contextlib.contextmanager\ndef create_tempdir():\n with tempfile.TemporaryDirectory() as dirname:\n with chdir(dirname):\n yield dirname\n\n\nclass TimeoutException(Exception):\n pass","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.swallow_io","uri":"program://CodeT/function/CodeT.src._execution.swallow_io#L163-L168","kind":"function","name":"swallow_io","path":"CodeT/src/_execution.py","language":"python","start_line":163,"end_line":168,"context_start_line":143,"context_end_line":188,"code":" return dict(\n task_id=task_id,\n passed=result[0] == \"passed\",\n result=result[0],\n completion=completion,\n )\n\n@contextlib.contextmanager\ndef time_limit(seconds: float):\n def signal_handler(signum, frame):\n raise TimeoutException(\"Timed out!\")\n signal.setitimer(signal.ITIMER_REAL, seconds)\n signal.signal(signal.SIGALRM, signal_handler)\n try:\n yield\n finally:\n signal.setitimer(signal.ITIMER_REAL, 0)\n\n\n@contextlib.contextmanager\ndef swallow_io():\n stream = WriteOnlyStringIO()\n with contextlib.redirect_stdout(stream):\n with contextlib.redirect_stderr(stream):\n with redirect_stdin(stream):\n yield\n\n\n@contextlib.contextmanager\ndef create_tempdir():\n with tempfile.TemporaryDirectory() as dirname:\n with chdir(dirname):\n yield dirname\n\n\nclass TimeoutException(Exception):\n pass\n\n\nclass WriteOnlyStringIO(io.StringIO):\n \"\"\" StringIO that throws an exception when it's read from \"\"\"\n\n def read(self, *args, **kwargs):\n raise IOError\n\n def readline(self, *args, **kwargs):","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.create_tempdir","uri":"program://CodeT/function/CodeT.src._execution.create_tempdir#L172-L175","kind":"function","name":"create_tempdir","path":"CodeT/src/_execution.py","language":"python","start_line":172,"end_line":175,"context_start_line":152,"context_end_line":195,"code":" def signal_handler(signum, frame):\n raise TimeoutException(\"Timed out!\")\n signal.setitimer(signal.ITIMER_REAL, seconds)\n signal.signal(signal.SIGALRM, signal_handler)\n try:\n yield\n finally:\n signal.setitimer(signal.ITIMER_REAL, 0)\n\n\n@contextlib.contextmanager\ndef swallow_io():\n stream = WriteOnlyStringIO()\n with contextlib.redirect_stdout(stream):\n with contextlib.redirect_stderr(stream):\n with redirect_stdin(stream):\n yield\n\n\n@contextlib.contextmanager\ndef create_tempdir():\n with tempfile.TemporaryDirectory() as dirname:\n with chdir(dirname):\n yield dirname\n\n\nclass TimeoutException(Exception):\n pass\n\n\nclass WriteOnlyStringIO(io.StringIO):\n \"\"\" StringIO that throws an exception when it's read from \"\"\"\n\n def read(self, *args, **kwargs):\n raise IOError\n\n def readline(self, *args, **kwargs):\n raise IOError\n\n def readlines(self, *args, **kwargs):\n raise IOError\n\n def readable(self, *args, **kwargs):\n \"\"\" Returns True if the IO object can be read. \"\"\"","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.TimeoutException","uri":"program://CodeT/class/CodeT.src._execution.TimeoutException#L178-L179","kind":"class","name":"TimeoutException","path":"CodeT/src/_execution.py","language":"python","start_line":178,"end_line":179,"context_start_line":158,"context_end_line":199,"code":" finally:\n signal.setitimer(signal.ITIMER_REAL, 0)\n\n\n@contextlib.contextmanager\ndef swallow_io():\n stream = WriteOnlyStringIO()\n with contextlib.redirect_stdout(stream):\n with contextlib.redirect_stderr(stream):\n with redirect_stdin(stream):\n yield\n\n\n@contextlib.contextmanager\ndef create_tempdir():\n with tempfile.TemporaryDirectory() as dirname:\n with chdir(dirname):\n yield dirname\n\n\nclass TimeoutException(Exception):\n pass\n\n\nclass WriteOnlyStringIO(io.StringIO):\n \"\"\" StringIO that throws an exception when it's read from \"\"\"\n\n def read(self, *args, **kwargs):\n raise IOError\n\n def readline(self, *args, **kwargs):\n raise IOError\n\n def readlines(self, *args, **kwargs):\n raise IOError\n\n def readable(self, *args, **kwargs):\n \"\"\" Returns True if the IO object can be read. \"\"\"\n return False\n\n\nclass redirect_stdin(contextlib._RedirectStream): # type: ignore","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.WriteOnlyStringIO","uri":"program://CodeT/class/CodeT.src._execution.WriteOnlyStringIO#L182-L196","kind":"class","name":"WriteOnlyStringIO","path":"CodeT/src/_execution.py","language":"python","start_line":182,"end_line":196,"context_start_line":162,"context_end_line":216,"code":"@contextlib.contextmanager\ndef swallow_io():\n stream = WriteOnlyStringIO()\n with contextlib.redirect_stdout(stream):\n with contextlib.redirect_stderr(stream):\n with redirect_stdin(stream):\n yield\n\n\n@contextlib.contextmanager\ndef create_tempdir():\n with tempfile.TemporaryDirectory() as dirname:\n with chdir(dirname):\n yield dirname\n\n\nclass TimeoutException(Exception):\n pass\n\n\nclass WriteOnlyStringIO(io.StringIO):\n \"\"\" StringIO that throws an exception when it's read from \"\"\"\n\n def read(self, *args, **kwargs):\n raise IOError\n\n def readline(self, *args, **kwargs):\n raise IOError\n\n def readlines(self, *args, **kwargs):\n raise IOError\n\n def readable(self, *args, **kwargs):\n \"\"\" Returns True if the IO object can be read. \"\"\"\n return False\n\n\nclass redirect_stdin(contextlib._RedirectStream): # type: ignore\n _stream = 'stdin'\n\n\n@contextlib.contextmanager\ndef chdir(root):\n if root == \".\":\n yield\n return\n cwd = os.getcwd()\n os.chdir(root)\n try:\n yield\n except BaseException as exc:\n raise exc\n finally:\n os.chdir(cwd)\n","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.redirect_stdin","uri":"program://CodeT/class/CodeT.src._execution.redirect_stdin#L199-L200","kind":"class","name":"redirect_stdin","path":"CodeT/src/_execution.py","language":"python","start_line":199,"end_line":200,"context_start_line":179,"context_end_line":220,"code":" pass\n\n\nclass WriteOnlyStringIO(io.StringIO):\n \"\"\" StringIO that throws an exception when it's read from \"\"\"\n\n def read(self, *args, **kwargs):\n raise IOError\n\n def readline(self, *args, **kwargs):\n raise IOError\n\n def readlines(self, *args, **kwargs):\n raise IOError\n\n def readable(self, *args, **kwargs):\n \"\"\" Returns True if the IO object can be read. \"\"\"\n return False\n\n\nclass redirect_stdin(contextlib._RedirectStream): # type: ignore\n _stream = 'stdin'\n\n\n@contextlib.contextmanager\ndef chdir(root):\n if root == \".\":\n yield\n return\n cwd = os.getcwd()\n os.chdir(root)\n try:\n yield\n except BaseException as exc:\n raise exc\n finally:\n os.chdir(cwd)\n\n\ndef reliability_guard(maximum_memory_bytes: Optional[int] = None):\n \"\"\"\n This disables various destructive functions and prevents the generated code","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.chdir","uri":"program://CodeT/function/CodeT.src._execution.chdir#L204-L215","kind":"function","name":"chdir","path":"CodeT/src/_execution.py","language":"python","start_line":204,"end_line":215,"context_start_line":184,"context_end_line":235,"code":"\n def read(self, *args, **kwargs):\n raise IOError\n\n def readline(self, *args, **kwargs):\n raise IOError\n\n def readlines(self, *args, **kwargs):\n raise IOError\n\n def readable(self, *args, **kwargs):\n \"\"\" Returns True if the IO object can be read. \"\"\"\n return False\n\n\nclass redirect_stdin(contextlib._RedirectStream): # type: ignore\n _stream = 'stdin'\n\n\n@contextlib.contextmanager\ndef chdir(root):\n if root == \".\":\n yield\n return\n cwd = os.getcwd()\n os.chdir(root)\n try:\n yield\n except BaseException as exc:\n raise exc\n finally:\n os.chdir(cwd)\n\n\ndef reliability_guard(maximum_memory_bytes: Optional[int] = None):\n \"\"\"\n This disables various destructive functions and prevents the generated code\n from interfering with the test (e.g. fork bomb, killing other processes,\n removing filesystem files, etc.)\n\n WARNING\n This function is NOT a security sandbox. Untrusted code, including, model-\n generated code, should not be blindly executed outside of one. See the \n Codex paper for more information about OpenAI's code sandbox, and proceed\n with caution.\n \"\"\"\n\n if maximum_memory_bytes is not None:\n import resource\n resource.setrlimit(resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes))\n resource.setrlimit(resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes))\n if not platform.uname().system == 'Darwin':","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.reliability_guard","uri":"program://CodeT/function/CodeT.src._execution.reliability_guard#L218-L290","kind":"function","name":"reliability_guard","path":"CodeT/src/_execution.py","language":"python","start_line":218,"end_line":290,"context_start_line":198,"context_end_line":290,"code":"\nclass redirect_stdin(contextlib._RedirectStream): # type: ignore\n _stream = 'stdin'\n\n\n@contextlib.contextmanager\ndef chdir(root):\n if root == \".\":\n yield\n return\n cwd = os.getcwd()\n os.chdir(root)\n try:\n yield\n except BaseException as exc:\n raise exc\n finally:\n os.chdir(cwd)\n\n\ndef reliability_guard(maximum_memory_bytes: Optional[int] = None):\n \"\"\"\n This disables various destructive functions and prevents the generated code\n from interfering with the test (e.g. fork bomb, killing other processes,\n removing filesystem files, etc.)\n\n WARNING\n This function is NOT a security sandbox. Untrusted code, including, model-\n generated code, should not be blindly executed outside of one. See the \n Codex paper for more information about OpenAI's code sandbox, and proceed\n with caution.\n \"\"\"\n\n if maximum_memory_bytes is not None:\n import resource\n resource.setrlimit(resource.RLIMIT_AS, (maximum_memory_bytes, maximum_memory_bytes))\n resource.setrlimit(resource.RLIMIT_DATA, (maximum_memory_bytes, maximum_memory_bytes))\n if not platform.uname().system == 'Darwin':\n resource.setrlimit(resource.RLIMIT_STACK, (maximum_memory_bytes, maximum_memory_bytes))\n\n faulthandler.disable()\n\n import builtins\n builtins.exit = None\n builtins.quit = None\n\n import os\n os.environ['OMP_NUM_THREADS'] = '1'\n\n os.kill = None\n os.system = None\n os.putenv = None\n os.remove = None\n os.removedirs = None\n os.rmdir = None\n os.fchdir = None\n os.setuid = None\n os.fork = None\n os.forkpty = None\n os.killpg = None\n os.rename = None\n os.renames = None\n os.truncate = None\n os.replace = None\n os.unlink = None\n os.fchmod = None\n os.fchown = None\n os.chmod = None\n os.chown = None\n os.chroot = None\n os.fchdir = None\n os.lchflags = None\n os.lchmod = None\n os.lchown = None\n os.getcwd = None\n os.chdir = None\n\n import shutil\n shutil.rmtree = None\n shutil.move = None\n shutil.chown = None\n\n import subprocess\n subprocess.Popen = None # type: ignore\n\n __builtins__['help'] = None\n\n import sys\n sys.modules['ipdb'] = None\n sys.modules['joblib'] = None\n sys.modules['resource'] = None\n sys.modules['psutil'] = None\n sys.modules['tkinter'] = None","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.unsafe_execute","uri":"program://CodeT/function/CodeT.src._execution.unsafe_execute#L96-L129","kind":"function","name":"unsafe_execute","path":"CodeT/src/_execution.py","language":"python","start_line":96,"end_line":129,"context_start_line":76,"context_end_line":149,"code":" if p.is_alive():\n p.kill()\n\n if not result:\n result.append(\"timed out\")\n\n return dict(\n task_id=task_id,\n test_cases=test_cases,\n completion=completion,\n passed=(type(result[0]) == list) and len(result[0]) > 0,\n result=result[0]\n )\n\ndef check_correctness(task_id: str, prompt: str, completion: str, test: str, entry_point: str, timeout: float) -> Dict:\n \"\"\"\n Evaluates the functional correctness of a completion by running the test\n suite provided in the problem. \n \"\"\"\n\n def unsafe_execute():\n\n with create_tempdir():\n\n # These system calls are needed when cleaning up tempdir.\n import os\n import shutil\n rmtree = shutil.rmtree\n rmdir = os.rmdir\n chdir = os.chdir\n\n # Disable functionalities that can make destructive changes to the test.\n reliability_guard()\n\n # Construct the check program and run it.\n check_program = (\n prompt + completion + \"\\n\" + test + \"\\n\" + f'check({entry_point})'\n )\n\n try:\n exec_globals = {}\n with swallow_io():\n with time_limit(timeout):\n exec(check_program, exec_globals)\n result.append(\"passed\")\n except TimeoutException:\n result.append(\"timed out\")\n except BaseException as e:\n result.append(f\"failed: {e}\")\n\n # Needed for cleaning up.\n shutil.rmtree = rmtree\n os.rmdir = rmdir\n os.chdir = chdir\n\n manager = multiprocessing.Manager()\n result = manager.list()\n\n p = multiprocessing.Process(target=unsafe_execute)\n p.start()\n p.join(timeout=timeout+1)\n if p.is_alive():\n p.kill()\n\n if not result:\n result.append(\"timed out\")\n\n return dict(\n task_id=task_id,\n passed=result[0] == \"passed\",\n result=result[0],\n completion=completion,\n )\n","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.signal_handler","uri":"program://CodeT/function/CodeT.src._execution.signal_handler#L152-L153","kind":"function","name":"signal_handler","path":"CodeT/src/_execution.py","language":"python","start_line":152,"end_line":153,"context_start_line":132,"context_end_line":173,"code":" result = manager.list()\n\n p = multiprocessing.Process(target=unsafe_execute)\n p.start()\n p.join(timeout=timeout+1)\n if p.is_alive():\n p.kill()\n\n if not result:\n result.append(\"timed out\")\n\n return dict(\n task_id=task_id,\n passed=result[0] == \"passed\",\n result=result[0],\n completion=completion,\n )\n\n@contextlib.contextmanager\ndef time_limit(seconds: float):\n def signal_handler(signum, frame):\n raise TimeoutException(\"Timed out!\")\n signal.setitimer(signal.ITIMER_REAL, seconds)\n signal.signal(signal.SIGALRM, signal_handler)\n try:\n yield\n finally:\n signal.setitimer(signal.ITIMER_REAL, 0)\n\n\n@contextlib.contextmanager\ndef swallow_io():\n stream = WriteOnlyStringIO()\n with contextlib.redirect_stdout(stream):\n with contextlib.redirect_stderr(stream):\n with redirect_stdin(stream):\n yield\n\n\n@contextlib.contextmanager\ndef create_tempdir():\n with tempfile.TemporaryDirectory() as dirname:","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.read","uri":"program://CodeT/function/CodeT.src._execution.read#L185-L186","kind":"function","name":"read","path":"CodeT/src/_execution.py","language":"python","start_line":185,"end_line":186,"context_start_line":165,"context_end_line":206,"code":" with contextlib.redirect_stdout(stream):\n with contextlib.redirect_stderr(stream):\n with redirect_stdin(stream):\n yield\n\n\n@contextlib.contextmanager\ndef create_tempdir():\n with tempfile.TemporaryDirectory() as dirname:\n with chdir(dirname):\n yield dirname\n\n\nclass TimeoutException(Exception):\n pass\n\n\nclass WriteOnlyStringIO(io.StringIO):\n \"\"\" StringIO that throws an exception when it's read from \"\"\"\n\n def read(self, *args, **kwargs):\n raise IOError\n\n def readline(self, *args, **kwargs):\n raise IOError\n\n def readlines(self, *args, **kwargs):\n raise IOError\n\n def readable(self, *args, **kwargs):\n \"\"\" Returns True if the IO object can be read. \"\"\"\n return False\n\n\nclass redirect_stdin(contextlib._RedirectStream): # type: ignore\n _stream = 'stdin'\n\n\n@contextlib.contextmanager\ndef chdir(root):\n if root == \".\":\n yield","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.readline","uri":"program://CodeT/function/CodeT.src._execution.readline#L188-L189","kind":"function","name":"readline","path":"CodeT/src/_execution.py","language":"python","start_line":188,"end_line":189,"context_start_line":168,"context_end_line":209,"code":" yield\n\n\n@contextlib.contextmanager\ndef create_tempdir():\n with tempfile.TemporaryDirectory() as dirname:\n with chdir(dirname):\n yield dirname\n\n\nclass TimeoutException(Exception):\n pass\n\n\nclass WriteOnlyStringIO(io.StringIO):\n \"\"\" StringIO that throws an exception when it's read from \"\"\"\n\n def read(self, *args, **kwargs):\n raise IOError\n\n def readline(self, *args, **kwargs):\n raise IOError\n\n def readlines(self, *args, **kwargs):\n raise IOError\n\n def readable(self, *args, **kwargs):\n \"\"\" Returns True if the IO object can be read. \"\"\"\n return False\n\n\nclass redirect_stdin(contextlib._RedirectStream): # type: ignore\n _stream = 'stdin'\n\n\n@contextlib.contextmanager\ndef chdir(root):\n if root == \".\":\n yield\n return\n cwd = os.getcwd()\n os.chdir(root)","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.readlines","uri":"program://CodeT/function/CodeT.src._execution.readlines#L191-L192","kind":"function","name":"readlines","path":"CodeT/src/_execution.py","language":"python","start_line":191,"end_line":192,"context_start_line":171,"context_end_line":212,"code":"@contextlib.contextmanager\ndef create_tempdir():\n with tempfile.TemporaryDirectory() as dirname:\n with chdir(dirname):\n yield dirname\n\n\nclass TimeoutException(Exception):\n pass\n\n\nclass WriteOnlyStringIO(io.StringIO):\n \"\"\" StringIO that throws an exception when it's read from \"\"\"\n\n def read(self, *args, **kwargs):\n raise IOError\n\n def readline(self, *args, **kwargs):\n raise IOError\n\n def readlines(self, *args, **kwargs):\n raise IOError\n\n def readable(self, *args, **kwargs):\n \"\"\" Returns True if the IO object can be read. \"\"\"\n return False\n\n\nclass redirect_stdin(contextlib._RedirectStream): # type: ignore\n _stream = 'stdin'\n\n\n@contextlib.contextmanager\ndef chdir(root):\n if root == \".\":\n yield\n return\n cwd = os.getcwd()\n os.chdir(root)\n try:\n yield\n except BaseException as exc:","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src._execution.readable","uri":"program://CodeT/function/CodeT.src._execution.readable#L194-L196","kind":"function","name":"readable","path":"CodeT/src/_execution.py","language":"python","start_line":194,"end_line":196,"context_start_line":174,"context_end_line":216,"code":" with chdir(dirname):\n yield dirname\n\n\nclass TimeoutException(Exception):\n pass\n\n\nclass WriteOnlyStringIO(io.StringIO):\n \"\"\" StringIO that throws an exception when it's read from \"\"\"\n\n def read(self, *args, **kwargs):\n raise IOError\n\n def readline(self, *args, **kwargs):\n raise IOError\n\n def readlines(self, *args, **kwargs):\n raise IOError\n\n def readable(self, *args, **kwargs):\n \"\"\" Returns True if the IO object can be read. \"\"\"\n return False\n\n\nclass redirect_stdin(contextlib._RedirectStream): # type: ignore\n _stream = 'stdin'\n\n\n@contextlib.contextmanager\ndef chdir(root):\n if root == \".\":\n yield\n return\n cwd = os.getcwd()\n os.chdir(root)\n try:\n yield\n except BaseException as exc:\n raise exc\n finally:\n os.chdir(cwd)\n","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.execution","uri":"program://CodeT/module/CodeT.src.execution#L1-L105","kind":"module","name":"CodeT.src.execution","path":"CodeT/src/execution.py","language":"python","start_line":1,"end_line":105,"context_start_line":1,"context_end_line":105,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport ctypes\nlibgcc_s = ctypes.CDLL('libgcc_s.so.1')\n\nfrom collections import defaultdict\nfrom concurrent.futures import as_completed, ProcessPoolExecutor\nimport logging\n\nfrom src._execution import check_correctness, check_correctness_with_test_cases\n\nlogging.basicConfig(\n format=\"SystemLog: [%(asctime)s][%(name)s][%(levelname)s] - %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n level=logging.INFO,\n)\n\nlogger = logging.getLogger(__name__)\n\ndef evaluate_with_test_code(\n samples,\n timeout\n):\n logger.info(f'Start evaluation with test code, timeout={timeout}')\n # Check the generated samples against test suites.\n with ProcessPoolExecutor() as executor:\n\n futures = []\n existed_completion = defaultdict(set)\n results = defaultdict(defaultdict)\n\n for sample in samples:\n task_id = sample[\"task_id\"]\n prompt = sample['prompt']\n test = sample['test']\n entry_point = sample['entry_point']\n completion = sample[\"completion\"]\n if completion in existed_completion[task_id]:\n continue\n existed_completion[task_id].add(completion)\n args = (task_id, prompt, completion, test, entry_point, timeout)\n future = executor.submit(check_correctness, *args)\n futures.append(future)\n logger.info(f'{len(futures)} execution requests are submitted')\n \n for idx, future in enumerate(as_completed(futures)):\n logger.info('[{}/{}] execution completed'.format(idx+1, len(futures)))\n result = future.result()\n results[result[\"task_id\"]][result[\"completion\"]] = result\n\n logger.info('execution finished! start parsing results')\n samples_with_result = []\n for sample in samples:\n task_id = sample[\"task_id\"]\n completion = sample[\"completion\"]\n result = results[task_id][completion]\n sample[\"result\"] = result[\"result\"]\n sample[\"passed\"] = result[\"passed\"]\n samples_with_result.append(sample)\n\n assert len(samples_with_result) == len(samples), \"Some problems are not attempted.\"\n\n return samples_with_result\n\ndef evaluate_with_test_cases(\n solutions,\n test_cases_dict,\n timeout,\n limit\n):\n logger.info(f'Start evaluation with test cases, timeout={timeout}, limit={limit}')\n # Check the generated solutions against test suites.\n with ProcessPoolExecutor() as executor:\n futures = []\n results_list = []\n existed_completion = defaultdict(set)\n\n for solution in solutions:\n task_id = solution['task_id']\n prompt = solution['prompt']\n completion = solution['completion']\n if completion in existed_completion[task_id]:\n continue\n existed_completion[task_id].add(completion)\n task_test_cases = test_cases_dict[task_id]\n if not task_test_cases:\n continue\n # get limited test cases\n limited_task_test_cases = [cases_per_sample[:limit] for cases_per_sample in task_test_cases]\n limited_task_test_cases = sum(limited_task_test_cases, [])\n \n args = (task_id, prompt, completion, list(set(limited_task_test_cases)), timeout)\n future = executor.submit(check_correctness_with_test_cases, *args)\n futures.append(future)\n\n logger.info(f'{len(futures)} execution requests are submitted')\n for idx, future in enumerate(as_completed(futures)):\n logger.info('[{}/{}] execution completed'.format(idx+1, len(futures)))\n result = future.result()\n results_list.append(result)\n\n logger.info('execution finished!')\n return results_list\n","source_hash":"7743c9a58e55de4bd4ca551e91417d80610fa686c0dfae5627624957d6868170","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.execution.evaluate_with_test_code","uri":"program://CodeT/function/CodeT.src.execution.evaluate_with_test_code#L21-L64","kind":"function","name":"evaluate_with_test_code","path":"CodeT/src/execution.py","language":"python","start_line":21,"end_line":64,"context_start_line":1,"context_end_line":84,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport ctypes\nlibgcc_s = ctypes.CDLL('libgcc_s.so.1')\n\nfrom collections import defaultdict\nfrom concurrent.futures import as_completed, ProcessPoolExecutor\nimport logging\n\nfrom src._execution import check_correctness, check_correctness_with_test_cases\n\nlogging.basicConfig(\n format=\"SystemLog: [%(asctime)s][%(name)s][%(levelname)s] - %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n level=logging.INFO,\n)\n\nlogger = logging.getLogger(__name__)\n\ndef evaluate_with_test_code(\n samples,\n timeout\n):\n logger.info(f'Start evaluation with test code, timeout={timeout}')\n # Check the generated samples against test suites.\n with ProcessPoolExecutor() as executor:\n\n futures = []\n existed_completion = defaultdict(set)\n results = defaultdict(defaultdict)\n\n for sample in samples:\n task_id = sample[\"task_id\"]\n prompt = sample['prompt']\n test = sample['test']\n entry_point = sample['entry_point']\n completion = sample[\"completion\"]\n if completion in existed_completion[task_id]:\n continue\n existed_completion[task_id].add(completion)\n args = (task_id, prompt, completion, test, entry_point, timeout)\n future = executor.submit(check_correctness, *args)\n futures.append(future)\n logger.info(f'{len(futures)} execution requests are submitted')\n \n for idx, future in enumerate(as_completed(futures)):\n logger.info('[{}/{}] execution completed'.format(idx+1, len(futures)))\n result = future.result()\n results[result[\"task_id\"]][result[\"completion\"]] = result\n\n logger.info('execution finished! start parsing results')\n samples_with_result = []\n for sample in samples:\n task_id = sample[\"task_id\"]\n completion = sample[\"completion\"]\n result = results[task_id][completion]\n sample[\"result\"] = result[\"result\"]\n sample[\"passed\"] = result[\"passed\"]\n samples_with_result.append(sample)\n\n assert len(samples_with_result) == len(samples), \"Some problems are not attempted.\"\n\n return samples_with_result\n\ndef evaluate_with_test_cases(\n solutions,\n test_cases_dict,\n timeout,\n limit\n):\n logger.info(f'Start evaluation with test cases, timeout={timeout}, limit={limit}')\n # Check the generated solutions against test suites.\n with ProcessPoolExecutor() as executor:\n futures = []\n results_list = []\n existed_completion = defaultdict(set)\n\n for solution in solutions:\n task_id = solution['task_id']\n prompt = solution['prompt']\n completion = solution['completion']\n if completion in existed_completion[task_id]:\n continue","source_hash":"7743c9a58e55de4bd4ca551e91417d80610fa686c0dfae5627624957d6868170","truncated":false} {"repo_id":"CodeT","entity_id":"py:CodeT.src.execution.evaluate_with_test_cases","uri":"program://CodeT/function/CodeT.src.execution.evaluate_with_test_cases#L66-L104","kind":"function","name":"evaluate_with_test_cases","path":"CodeT/src/execution.py","language":"python","start_line":66,"end_line":104,"context_start_line":46,"context_end_line":105,"code":" \n for idx, future in enumerate(as_completed(futures)):\n logger.info('[{}/{}] execution completed'.format(idx+1, len(futures)))\n result = future.result()\n results[result[\"task_id\"]][result[\"completion\"]] = result\n\n logger.info('execution finished! start parsing results')\n samples_with_result = []\n for sample in samples:\n task_id = sample[\"task_id\"]\n completion = sample[\"completion\"]\n result = results[task_id][completion]\n sample[\"result\"] = result[\"result\"]\n sample[\"passed\"] = result[\"passed\"]\n samples_with_result.append(sample)\n\n assert len(samples_with_result) == len(samples), \"Some problems are not attempted.\"\n\n return samples_with_result\n\ndef evaluate_with_test_cases(\n solutions,\n test_cases_dict,\n timeout,\n limit\n):\n logger.info(f'Start evaluation with test cases, timeout={timeout}, limit={limit}')\n # Check the generated solutions against test suites.\n with ProcessPoolExecutor() as executor:\n futures = []\n results_list = []\n existed_completion = defaultdict(set)\n\n for solution in solutions:\n task_id = solution['task_id']\n prompt = solution['prompt']\n completion = solution['completion']\n if completion in existed_completion[task_id]:\n continue\n existed_completion[task_id].add(completion)\n task_test_cases = test_cases_dict[task_id]\n if not task_test_cases:\n continue\n # get limited test cases\n limited_task_test_cases = [cases_per_sample[:limit] for cases_per_sample in task_test_cases]\n limited_task_test_cases = sum(limited_task_test_cases, [])\n \n args = (task_id, prompt, completion, list(set(limited_task_test_cases)), timeout)\n future = executor.submit(check_correctness_with_test_cases, *args)\n futures.append(future)\n\n logger.info(f'{len(futures)} execution requests are submitted')\n for idx, future in enumerate(as_completed(futures)):\n logger.info('[{}/{}] execution completed'.format(idx+1, len(futures)))\n result = future.result()\n results_list.append(result)\n\n logger.info('execution finished!')\n return results_list\n","source_hash":"7743c9a58e55de4bd4ca551e91417d80610fa686c0dfae5627624957d6868170","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_vector","uri":"program://CodeT/module/RepoCoder.build_vector#L1-L139","kind":"module","name":"RepoCoder.build_vector","path":"RepoCoder/build_vector.py","language":"python","start_line":1,"end_line":139,"context_start_line":1,"context_end_line":139,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport tqdm\nimport itertools\nfrom collections import defaultdict\nfrom concurrent.futures import as_completed, ProcessPoolExecutor\n\nfrom utils import Tools, FilePathBuilder, CONSTANTS\n\nclass BagOfWords:\n def __init__(self, input_file):\n self.input_file = input_file\n\n def build(self):\n print(f'building one gram vector for {self.input_file}')\n futures = dict()\n lines = Tools.load_pickle(self.input_file)\n with ProcessPoolExecutor(max_workers=48) as executor:\n for line in lines:\n futures[executor.submit(Tools.tokenize, line['context'])] = line\n \n new_lines = []\n t = tqdm.tqdm(total=len(futures))\n for future in as_completed(futures):\n line = futures[future]\n tokenized = future.result()\n new_lines.append({\n 'context': line['context'],\n 'metadata': line['metadata'],\n 'data': [{'embedding': tokenized}]\n })\n tqdm.tqdm.update(t)\n output_file_path = FilePathBuilder.one_gram_vector_path(self.input_file)\n Tools.dump_pickle(new_lines, output_file_path)\n\n\nclass BuildVectorWrapper:\n def __init__(self, benchmark, vector_builder, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n self.vector_builder = vector_builder\n self.benchmark = benchmark\n\n def vectorize_repo_windows(self):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n for repo in self.repos:\n builder = self.vector_builder(\n FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n )\n builder.build()\n\n def vectorize_baseline_and_ground_windows(self):\n for window_size in self.window_sizes:\n for repo in self.repos:\n builder = self.vector_builder(FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.rg, repo, window_size))\n builder.build()\n builder = self.vector_builder(FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.gt, repo, window_size))\n builder.build()\n\n def vectorize_prediction_windows(self, mode, prediction_path_template):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n prediction_path = prediction_path_template.format(window_size=window_size, slice_size=slice_size)\n for repo in self.repos:\n window_path = FilePathBuilder.gen_first_window_path(\n self.benchmark, mode, prediction_path, repo, window_size\n )\n builder = self.vector_builder(window_path)\n builder.build()\n\nclass BuildEmbeddingVector:\n '''\n utilize external embedding model to generate embedding vector\n '''\n def __init__(self, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n\n def build_input_file_for_repo_window(self, slice_size):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {\n 'window_file_path': file_path,\n 'original_metadata': line['metadata'],\n },})\n return lines\n\n def build_input_file_search_first_window(self, mode, benchmark):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.search_first_window_path(benchmark, mode, repo, window_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {\n 'window_file_path': file_path,\n 'original_metadata': line['metadata']\n }})\n return lines\n \n def build_input_file_for_gen_first_window(self, mode, benchmark, prediction_path):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.gen_first_window_path(benchmark, mode, prediction_path, repo, window_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {\n 'window_file_path': file_path,\n 'original_metadata': line['metadata']\n }})\n return lines\n\n @staticmethod\n def place_generated_embeddings(generated_embeddings):\n vector_file_path_to_lines = defaultdict(list)\n for line in generated_embeddings:\n window_path = line['metadata']['window_file_path']\n original_metadata = line['metadata']['original_metadata']\n vector_file_path = FilePathBuilder.ada002_vector_path(window_path)\n vector_file_path_to_lines[vector_file_path].append({\n 'context': line['context'],\n 'metadata': original_metadata,\n 'data': line['data']\n })\n for vector_file_path, lines in vector_file_path_to_lines.items():\n Tools.dump_pickle(lines, vector_file_path)","source_hash":"624930fae7b34d417eb8f8c1668788682cf0958c60ed4b7938dcdffdf7bfe18e","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_vector.BagOfWords","uri":"program://CodeT/class/RepoCoder.build_vector.BagOfWords#L11-L35","kind":"class","name":"BagOfWords","path":"RepoCoder/build_vector.py","language":"python","start_line":11,"end_line":35,"context_start_line":1,"context_end_line":55,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport tqdm\nimport itertools\nfrom collections import defaultdict\nfrom concurrent.futures import as_completed, ProcessPoolExecutor\n\nfrom utils import Tools, FilePathBuilder, CONSTANTS\n\nclass BagOfWords:\n def __init__(self, input_file):\n self.input_file = input_file\n\n def build(self):\n print(f'building one gram vector for {self.input_file}')\n futures = dict()\n lines = Tools.load_pickle(self.input_file)\n with ProcessPoolExecutor(max_workers=48) as executor:\n for line in lines:\n futures[executor.submit(Tools.tokenize, line['context'])] = line\n \n new_lines = []\n t = tqdm.tqdm(total=len(futures))\n for future in as_completed(futures):\n line = futures[future]\n tokenized = future.result()\n new_lines.append({\n 'context': line['context'],\n 'metadata': line['metadata'],\n 'data': [{'embedding': tokenized}]\n })\n tqdm.tqdm.update(t)\n output_file_path = FilePathBuilder.one_gram_vector_path(self.input_file)\n Tools.dump_pickle(new_lines, output_file_path)\n\n\nclass BuildVectorWrapper:\n def __init__(self, benchmark, vector_builder, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n self.vector_builder = vector_builder\n self.benchmark = benchmark\n\n def vectorize_repo_windows(self):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n for repo in self.repos:\n builder = self.vector_builder(\n FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n )\n builder.build()\n\n def vectorize_baseline_and_ground_windows(self):\n for window_size in self.window_sizes:","source_hash":"624930fae7b34d417eb8f8c1668788682cf0958c60ed4b7938dcdffdf7bfe18e","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_vector.BuildVectorWrapper","uri":"program://CodeT/class/RepoCoder.build_vector.BuildVectorWrapper#L38-L70","kind":"class","name":"BuildVectorWrapper","path":"RepoCoder/build_vector.py","language":"python","start_line":38,"end_line":70,"context_start_line":18,"context_end_line":90,"code":" lines = Tools.load_pickle(self.input_file)\n with ProcessPoolExecutor(max_workers=48) as executor:\n for line in lines:\n futures[executor.submit(Tools.tokenize, line['context'])] = line\n \n new_lines = []\n t = tqdm.tqdm(total=len(futures))\n for future in as_completed(futures):\n line = futures[future]\n tokenized = future.result()\n new_lines.append({\n 'context': line['context'],\n 'metadata': line['metadata'],\n 'data': [{'embedding': tokenized}]\n })\n tqdm.tqdm.update(t)\n output_file_path = FilePathBuilder.one_gram_vector_path(self.input_file)\n Tools.dump_pickle(new_lines, output_file_path)\n\n\nclass BuildVectorWrapper:\n def __init__(self, benchmark, vector_builder, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n self.vector_builder = vector_builder\n self.benchmark = benchmark\n\n def vectorize_repo_windows(self):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n for repo in self.repos:\n builder = self.vector_builder(\n FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n )\n builder.build()\n\n def vectorize_baseline_and_ground_windows(self):\n for window_size in self.window_sizes:\n for repo in self.repos:\n builder = self.vector_builder(FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.rg, repo, window_size))\n builder.build()\n builder = self.vector_builder(FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.gt, repo, window_size))\n builder.build()\n\n def vectorize_prediction_windows(self, mode, prediction_path_template):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n prediction_path = prediction_path_template.format(window_size=window_size, slice_size=slice_size)\n for repo in self.repos:\n window_path = FilePathBuilder.gen_first_window_path(\n self.benchmark, mode, prediction_path, repo, window_size\n )\n builder = self.vector_builder(window_path)\n builder.build()\n\nclass BuildEmbeddingVector:\n '''\n utilize external embedding model to generate embedding vector\n '''\n def __init__(self, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n\n def build_input_file_for_repo_window(self, slice_size):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {","source_hash":"624930fae7b34d417eb8f8c1668788682cf0958c60ed4b7938dcdffdf7bfe18e","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_vector.BuildEmbeddingVector","uri":"program://CodeT/class/RepoCoder.build_vector.BuildEmbeddingVector#L72-L139","kind":"class","name":"BuildEmbeddingVector","path":"RepoCoder/build_vector.py","language":"python","start_line":72,"end_line":139,"context_start_line":52,"context_end_line":139,"code":" builder.build()\n\n def vectorize_baseline_and_ground_windows(self):\n for window_size in self.window_sizes:\n for repo in self.repos:\n builder = self.vector_builder(FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.rg, repo, window_size))\n builder.build()\n builder = self.vector_builder(FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.gt, repo, window_size))\n builder.build()\n\n def vectorize_prediction_windows(self, mode, prediction_path_template):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n prediction_path = prediction_path_template.format(window_size=window_size, slice_size=slice_size)\n for repo in self.repos:\n window_path = FilePathBuilder.gen_first_window_path(\n self.benchmark, mode, prediction_path, repo, window_size\n )\n builder = self.vector_builder(window_path)\n builder.build()\n\nclass BuildEmbeddingVector:\n '''\n utilize external embedding model to generate embedding vector\n '''\n def __init__(self, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n\n def build_input_file_for_repo_window(self, slice_size):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {\n 'window_file_path': file_path,\n 'original_metadata': line['metadata'],\n },})\n return lines\n\n def build_input_file_search_first_window(self, mode, benchmark):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.search_first_window_path(benchmark, mode, repo, window_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {\n 'window_file_path': file_path,\n 'original_metadata': line['metadata']\n }})\n return lines\n \n def build_input_file_for_gen_first_window(self, mode, benchmark, prediction_path):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.gen_first_window_path(benchmark, mode, prediction_path, repo, window_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {\n 'window_file_path': file_path,\n 'original_metadata': line['metadata']\n }})\n return lines\n\n @staticmethod\n def place_generated_embeddings(generated_embeddings):\n vector_file_path_to_lines = defaultdict(list)\n for line in generated_embeddings:\n window_path = line['metadata']['window_file_path']\n original_metadata = line['metadata']['original_metadata']\n vector_file_path = FilePathBuilder.ada002_vector_path(window_path)\n vector_file_path_to_lines[vector_file_path].append({\n 'context': line['context'],\n 'metadata': original_metadata,\n 'data': line['data']\n })\n for vector_file_path, lines in vector_file_path_to_lines.items():\n Tools.dump_pickle(lines, vector_file_path)","source_hash":"624930fae7b34d417eb8f8c1668788682cf0958c60ed4b7938dcdffdf7bfe18e","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_vector.__init__","uri":"program://CodeT/function/RepoCoder.build_vector.__init__#L76-L79","kind":"function","name":"__init__","path":"RepoCoder/build_vector.py","language":"python","start_line":76,"end_line":79,"context_start_line":56,"context_end_line":99,"code":" for repo in self.repos:\n builder = self.vector_builder(FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.rg, repo, window_size))\n builder.build()\n builder = self.vector_builder(FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.gt, repo, window_size))\n builder.build()\n\n def vectorize_prediction_windows(self, mode, prediction_path_template):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n prediction_path = prediction_path_template.format(window_size=window_size, slice_size=slice_size)\n for repo in self.repos:\n window_path = FilePathBuilder.gen_first_window_path(\n self.benchmark, mode, prediction_path, repo, window_size\n )\n builder = self.vector_builder(window_path)\n builder.build()\n\nclass BuildEmbeddingVector:\n '''\n utilize external embedding model to generate embedding vector\n '''\n def __init__(self, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n\n def build_input_file_for_repo_window(self, slice_size):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {\n 'window_file_path': file_path,\n 'original_metadata': line['metadata'],\n },})\n return lines\n\n def build_input_file_search_first_window(self, mode, benchmark):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:","source_hash":"624930fae7b34d417eb8f8c1668788682cf0958c60ed4b7938dcdffdf7bfe18e","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_vector.build","uri":"program://CodeT/function/RepoCoder.build_vector.build#L15-L35","kind":"function","name":"build","path":"RepoCoder/build_vector.py","language":"python","start_line":15,"end_line":35,"context_start_line":1,"context_end_line":55,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport tqdm\nimport itertools\nfrom collections import defaultdict\nfrom concurrent.futures import as_completed, ProcessPoolExecutor\n\nfrom utils import Tools, FilePathBuilder, CONSTANTS\n\nclass BagOfWords:\n def __init__(self, input_file):\n self.input_file = input_file\n\n def build(self):\n print(f'building one gram vector for {self.input_file}')\n futures = dict()\n lines = Tools.load_pickle(self.input_file)\n with ProcessPoolExecutor(max_workers=48) as executor:\n for line in lines:\n futures[executor.submit(Tools.tokenize, line['context'])] = line\n \n new_lines = []\n t = tqdm.tqdm(total=len(futures))\n for future in as_completed(futures):\n line = futures[future]\n tokenized = future.result()\n new_lines.append({\n 'context': line['context'],\n 'metadata': line['metadata'],\n 'data': [{'embedding': tokenized}]\n })\n tqdm.tqdm.update(t)\n output_file_path = FilePathBuilder.one_gram_vector_path(self.input_file)\n Tools.dump_pickle(new_lines, output_file_path)\n\n\nclass BuildVectorWrapper:\n def __init__(self, benchmark, vector_builder, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n self.vector_builder = vector_builder\n self.benchmark = benchmark\n\n def vectorize_repo_windows(self):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n for repo in self.repos:\n builder = self.vector_builder(\n FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n )\n builder.build()\n\n def vectorize_baseline_and_ground_windows(self):\n for window_size in self.window_sizes:","source_hash":"624930fae7b34d417eb8f8c1668788682cf0958c60ed4b7938dcdffdf7bfe18e","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_vector.vectorize_repo_windows","uri":"program://CodeT/function/RepoCoder.build_vector.vectorize_repo_windows#L46-L52","kind":"function","name":"vectorize_repo_windows","path":"RepoCoder/build_vector.py","language":"python","start_line":46,"end_line":52,"context_start_line":26,"context_end_line":72,"code":" line = futures[future]\n tokenized = future.result()\n new_lines.append({\n 'context': line['context'],\n 'metadata': line['metadata'],\n 'data': [{'embedding': tokenized}]\n })\n tqdm.tqdm.update(t)\n output_file_path = FilePathBuilder.one_gram_vector_path(self.input_file)\n Tools.dump_pickle(new_lines, output_file_path)\n\n\nclass BuildVectorWrapper:\n def __init__(self, benchmark, vector_builder, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n self.vector_builder = vector_builder\n self.benchmark = benchmark\n\n def vectorize_repo_windows(self):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n for repo in self.repos:\n builder = self.vector_builder(\n FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n )\n builder.build()\n\n def vectorize_baseline_and_ground_windows(self):\n for window_size in self.window_sizes:\n for repo in self.repos:\n builder = self.vector_builder(FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.rg, repo, window_size))\n builder.build()\n builder = self.vector_builder(FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.gt, repo, window_size))\n builder.build()\n\n def vectorize_prediction_windows(self, mode, prediction_path_template):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n prediction_path = prediction_path_template.format(window_size=window_size, slice_size=slice_size)\n for repo in self.repos:\n window_path = FilePathBuilder.gen_first_window_path(\n self.benchmark, mode, prediction_path, repo, window_size\n )\n builder = self.vector_builder(window_path)\n builder.build()\n\nclass BuildEmbeddingVector:","source_hash":"624930fae7b34d417eb8f8c1668788682cf0958c60ed4b7938dcdffdf7bfe18e","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_vector.vectorize_baseline_and_ground_windows","uri":"program://CodeT/function/RepoCoder.build_vector.vectorize_baseline_and_ground_windows#L54-L60","kind":"function","name":"vectorize_baseline_and_ground_windows","path":"RepoCoder/build_vector.py","language":"python","start_line":54,"end_line":60,"context_start_line":34,"context_end_line":80,"code":" output_file_path = FilePathBuilder.one_gram_vector_path(self.input_file)\n Tools.dump_pickle(new_lines, output_file_path)\n\n\nclass BuildVectorWrapper:\n def __init__(self, benchmark, vector_builder, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n self.vector_builder = vector_builder\n self.benchmark = benchmark\n\n def vectorize_repo_windows(self):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n for repo in self.repos:\n builder = self.vector_builder(\n FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n )\n builder.build()\n\n def vectorize_baseline_and_ground_windows(self):\n for window_size in self.window_sizes:\n for repo in self.repos:\n builder = self.vector_builder(FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.rg, repo, window_size))\n builder.build()\n builder = self.vector_builder(FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.gt, repo, window_size))\n builder.build()\n\n def vectorize_prediction_windows(self, mode, prediction_path_template):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n prediction_path = prediction_path_template.format(window_size=window_size, slice_size=slice_size)\n for repo in self.repos:\n window_path = FilePathBuilder.gen_first_window_path(\n self.benchmark, mode, prediction_path, repo, window_size\n )\n builder = self.vector_builder(window_path)\n builder.build()\n\nclass BuildEmbeddingVector:\n '''\n utilize external embedding model to generate embedding vector\n '''\n def __init__(self, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n","source_hash":"624930fae7b34d417eb8f8c1668788682cf0958c60ed4b7938dcdffdf7bfe18e","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_vector.vectorize_prediction_windows","uri":"program://CodeT/function/RepoCoder.build_vector.vectorize_prediction_windows#L62-L70","kind":"function","name":"vectorize_prediction_windows","path":"RepoCoder/build_vector.py","language":"python","start_line":62,"end_line":70,"context_start_line":42,"context_end_line":90,"code":" self.slice_sizes = slice_sizes\n self.vector_builder = vector_builder\n self.benchmark = benchmark\n\n def vectorize_repo_windows(self):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n for repo in self.repos:\n builder = self.vector_builder(\n FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n )\n builder.build()\n\n def vectorize_baseline_and_ground_windows(self):\n for window_size in self.window_sizes:\n for repo in self.repos:\n builder = self.vector_builder(FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.rg, repo, window_size))\n builder.build()\n builder = self.vector_builder(FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.gt, repo, window_size))\n builder.build()\n\n def vectorize_prediction_windows(self, mode, prediction_path_template):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n prediction_path = prediction_path_template.format(window_size=window_size, slice_size=slice_size)\n for repo in self.repos:\n window_path = FilePathBuilder.gen_first_window_path(\n self.benchmark, mode, prediction_path, repo, window_size\n )\n builder = self.vector_builder(window_path)\n builder.build()\n\nclass BuildEmbeddingVector:\n '''\n utilize external embedding model to generate embedding vector\n '''\n def __init__(self, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n\n def build_input_file_for_repo_window(self, slice_size):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {","source_hash":"624930fae7b34d417eb8f8c1668788682cf0958c60ed4b7938dcdffdf7bfe18e","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_vector.build_input_file_for_repo_window","uri":"program://CodeT/function/RepoCoder.build_vector.build_input_file_for_repo_window#L81-L94","kind":"function","name":"build_input_file_for_repo_window","path":"RepoCoder/build_vector.py","language":"python","start_line":81,"end_line":94,"context_start_line":61,"context_end_line":114,"code":"\n def vectorize_prediction_windows(self, mode, prediction_path_template):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n prediction_path = prediction_path_template.format(window_size=window_size, slice_size=slice_size)\n for repo in self.repos:\n window_path = FilePathBuilder.gen_first_window_path(\n self.benchmark, mode, prediction_path, repo, window_size\n )\n builder = self.vector_builder(window_path)\n builder.build()\n\nclass BuildEmbeddingVector:\n '''\n utilize external embedding model to generate embedding vector\n '''\n def __init__(self, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n\n def build_input_file_for_repo_window(self, slice_size):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {\n 'window_file_path': file_path,\n 'original_metadata': line['metadata'],\n },})\n return lines\n\n def build_input_file_search_first_window(self, mode, benchmark):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.search_first_window_path(benchmark, mode, repo, window_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {\n 'window_file_path': file_path,\n 'original_metadata': line['metadata']\n }})\n return lines\n \n def build_input_file_for_gen_first_window(self, mode, benchmark, prediction_path):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:","source_hash":"624930fae7b34d417eb8f8c1668788682cf0958c60ed4b7938dcdffdf7bfe18e","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_vector.build_input_file_search_first_window","uri":"program://CodeT/function/RepoCoder.build_vector.build_input_file_search_first_window#L96-L109","kind":"function","name":"build_input_file_search_first_window","path":"RepoCoder/build_vector.py","language":"python","start_line":96,"end_line":109,"context_start_line":76,"context_end_line":129,"code":" def __init__(self, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n\n def build_input_file_for_repo_window(self, slice_size):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {\n 'window_file_path': file_path,\n 'original_metadata': line['metadata'],\n },})\n return lines\n\n def build_input_file_search_first_window(self, mode, benchmark):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.search_first_window_path(benchmark, mode, repo, window_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {\n 'window_file_path': file_path,\n 'original_metadata': line['metadata']\n }})\n return lines\n \n def build_input_file_for_gen_first_window(self, mode, benchmark, prediction_path):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.gen_first_window_path(benchmark, mode, prediction_path, repo, window_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {\n 'window_file_path': file_path,\n 'original_metadata': line['metadata']\n }})\n return lines\n\n @staticmethod\n def place_generated_embeddings(generated_embeddings):\n vector_file_path_to_lines = defaultdict(list)\n for line in generated_embeddings:","source_hash":"624930fae7b34d417eb8f8c1668788682cf0958c60ed4b7938dcdffdf7bfe18e","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_vector.build_input_file_for_gen_first_window","uri":"program://CodeT/function/RepoCoder.build_vector.build_input_file_for_gen_first_window#L111-L124","kind":"function","name":"build_input_file_for_gen_first_window","path":"RepoCoder/build_vector.py","language":"python","start_line":111,"end_line":124,"context_start_line":91,"context_end_line":139,"code":" 'window_file_path': file_path,\n 'original_metadata': line['metadata'],\n },})\n return lines\n\n def build_input_file_search_first_window(self, mode, benchmark):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.search_first_window_path(benchmark, mode, repo, window_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {\n 'window_file_path': file_path,\n 'original_metadata': line['metadata']\n }})\n return lines\n \n def build_input_file_for_gen_first_window(self, mode, benchmark, prediction_path):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.gen_first_window_path(benchmark, mode, prediction_path, repo, window_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {\n 'window_file_path': file_path,\n 'original_metadata': line['metadata']\n }})\n return lines\n\n @staticmethod\n def place_generated_embeddings(generated_embeddings):\n vector_file_path_to_lines = defaultdict(list)\n for line in generated_embeddings:\n window_path = line['metadata']['window_file_path']\n original_metadata = line['metadata']['original_metadata']\n vector_file_path = FilePathBuilder.ada002_vector_path(window_path)\n vector_file_path_to_lines[vector_file_path].append({\n 'context': line['context'],\n 'metadata': original_metadata,\n 'data': line['data']\n })\n for vector_file_path, lines in vector_file_path_to_lines.items():\n Tools.dump_pickle(lines, vector_file_path)","source_hash":"624930fae7b34d417eb8f8c1668788682cf0958c60ed4b7938dcdffdf7bfe18e","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_vector.place_generated_embeddings","uri":"program://CodeT/function/RepoCoder.build_vector.place_generated_embeddings#L127-L139","kind":"function","name":"place_generated_embeddings","path":"RepoCoder/build_vector.py","language":"python","start_line":127,"end_line":139,"context_start_line":107,"context_end_line":139,"code":" 'original_metadata': line['metadata']\n }})\n return lines\n \n def build_input_file_for_gen_first_window(self, mode, benchmark, prediction_path):\n lines = []\n for window_size in self.window_sizes:\n for repo in self.repos:\n file_path = FilePathBuilder.gen_first_window_path(benchmark, mode, prediction_path, repo, window_size)\n loaded_lines = Tools.load_pickle(file_path)\n for line in loaded_lines:\n lines.append({\n 'context': line['context'],\n 'metadata': {\n 'window_file_path': file_path,\n 'original_metadata': line['metadata']\n }})\n return lines\n\n @staticmethod\n def place_generated_embeddings(generated_embeddings):\n vector_file_path_to_lines = defaultdict(list)\n for line in generated_embeddings:\n window_path = line['metadata']['window_file_path']\n original_metadata = line['metadata']['original_metadata']\n vector_file_path = FilePathBuilder.ada002_vector_path(window_path)\n vector_file_path_to_lines[vector_file_path].append({\n 'context': line['context'],\n 'metadata': original_metadata,\n 'data': line['data']\n })\n for vector_file_path, lines in vector_file_path_to_lines.items():\n Tools.dump_pickle(lines, vector_file_path)","source_hash":"624930fae7b34d417eb8f8c1668788682cf0958c60ed4b7938dcdffdf7bfe18e","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.search_code","uri":"program://CodeT/module/RepoCoder.search_code#L1-L122","kind":"module","name":"RepoCoder.search_code","path":"RepoCoder/search_code.py","language":"python","start_line":1,"end_line":122,"context_start_line":1,"context_end_line":122,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom concurrent.futures import as_completed, ProcessPoolExecutor\nimport numpy as np\nimport scipy\nimport tqdm\nimport os\nimport copy\nimport functools\n\nfrom utils import Tools, FilePathBuilder, CONSTANTS\n\nclass SimilarityScore:\n @staticmethod\n def cosine_similarity(embedding_vec1, embedding_vec2):\n return 1 - scipy.spatial.distance.cosine(embedding_vec1, embedding_vec2)\n \n @staticmethod\n def jaccard_similarity(list1, list2):\n set1 = set(list1)\n set2 = set(list2)\n intersection = len(set1.intersection(set2))\n union = len(set1.union(set2))\n return float(intersection) / union\n\nclass CodeSearchWorker:\n def __init__(self, repo_embedding_lines, query_embedding_lines, output_path, sim_scorer, max_top_k, log_message):\n self.repo_embedding_lines = repo_embedding_lines # list\n self.query_embedding_lines = query_embedding_lines # list\n self.max_top_k = max_top_k\n self.sim_scorer = sim_scorer\n self.output_path = output_path\n self.log_message = log_message\n \n def _is_context_after_hole(self, repo_embedding_line, query_line):\n hole_fpath_tuple = tuple(query_line['metadata']['fpath_tuple'])\n context_is_not_after_hole = []\n for metadata in repo_embedding_line['metadata']:\n if tuple(metadata['fpath_tuple']) != hole_fpath_tuple:\n context_is_not_after_hole.append(True)\n continue\n # now we know that the repo line is in the same file as the hole\n if metadata['end_line_no'] <= query_line['metadata']['context_start_lineno']:\n context_is_not_after_hole.append(True)\n continue\n context_is_not_after_hole.append(False)\n return not any(context_is_not_after_hole)\n \n def _find_top_k_context(self, query_line):\n top_k_context = []\n query_embedding = np.array(query_line['data'][0]['embedding'])\n for repo_embedding_line in self.repo_embedding_lines:\n if self._is_context_after_hole(repo_embedding_line, query_line):\n continue\n repo_line_embedding = np.array(repo_embedding_line['data'][0]['embedding'])\n similarity_score = self.sim_scorer(query_embedding, repo_line_embedding)\n top_k_context.append((repo_embedding_line, similarity_score))\n top_k_context = sorted(top_k_context, key=lambda x: x[1], reverse=False)[-self.max_top_k:]\n return top_k_context\n\n def run(self):\n query_lines_with_retrieved_results = []\n for query_line in self.query_embedding_lines:\n new_line = copy.deepcopy(query_line)\n top_k_context = self._find_top_k_context(new_line)\n new_line['top_k_context'] = top_k_context\n query_lines_with_retrieved_results.append(new_line)\n Tools.dump_pickle(query_lines_with_retrieved_results, self.output_path)\n\n\nclass CodeSearchWrapper:\n def __init__(self, vectorizer, benchmark, repos, window_sizes, slice_sizes):\n self.vectorizer = vectorizer\n if vectorizer == 'one-gram':\n self.sim_scorer = SimilarityScore.jaccard_similarity\n self.vector_path_builder = FilePathBuilder.one_gram_vector_path\n elif vectorizer == 'ada002':\n self.sim_scorer = SimilarityScore.cosine_similarity\n self.vector_path_builder = FilePathBuilder.ada002_vector_path\n self.max_top_k = 20 # store 20 top k context for the prompt construction (top 10)\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n self.benchmark = benchmark\n \n def _run_parallel(self, query_window_path_builder, prediction_path_template=None):\n workers = []\n for window_size in self.window_sizes:\n for slice_size in self.slice_sizes:\n for repo in self.repos:\n if prediction_path_template:\n query_window_path = query_window_path_builder(\n prediction_path_template.format(window_size=window_size, slice_size=slice_size),\n repo, window_size\n )\n else:\n query_window_path = query_window_path_builder(repo, window_size)\n query_line_path = self.vector_path_builder(query_window_path)\n repo_window_path = FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n repo_embedding_path = self.vector_path_builder(repo_window_path)\n output_path = FilePathBuilder.retrieval_results_path(query_line_path, repo_embedding_path, self.max_top_k)\n repo_embedding_lines = Tools.load_pickle(repo_embedding_path)\n query_embedding_lines = Tools.load_pickle(query_line_path)\n log_message = f'repo: {repo}, window: {window_size}, slice: {slice_size} {self.vectorizer}, max_top_k: {self.max_top_k}'\n worker = CodeSearchWorker(repo_embedding_lines, query_embedding_lines, output_path, self.sim_scorer, self.max_top_k, log_message)\n workers.append(worker)\n # process pool\n with ProcessPoolExecutor(max_workers=os.cpu_count()) as executor:\n futures = {executor.submit(worker.run, ) for worker in workers}\n for future in tqdm.tqdm(as_completed(futures), total=len(futures)):\n future.result()\n\n def search_baseline_and_ground(self):\n query_line_path_temp = functools.partial(FilePathBuilder.search_first_window_path, self.benchmark, CONSTANTS.rg)\n self._run_parallel(query_line_path_temp)\n query_line_path_temp = functools.partial(FilePathBuilder.search_first_window_path, self.benchmark, CONSTANTS.gt)\n self._run_parallel(query_line_path_temp)\n \n def search_prediction(self, mode, prediction_path_template):\n query_line_path_temp = functools.partial(FilePathBuilder.gen_first_window_path, self.benchmark, mode)\n self._run_parallel(query_line_path_temp, prediction_path_template)","source_hash":"977bc3469390b030dc2f47cd8a9b4449c9142308e58afe2b90d0fecad7359ba3","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.search_code.SimilarityScore","uri":"program://CodeT/class/RepoCoder.search_code.SimilarityScore#L14-L25","kind":"class","name":"SimilarityScore","path":"RepoCoder/search_code.py","language":"python","start_line":14,"end_line":25,"context_start_line":1,"context_end_line":45,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom concurrent.futures import as_completed, ProcessPoolExecutor\nimport numpy as np\nimport scipy\nimport tqdm\nimport os\nimport copy\nimport functools\n\nfrom utils import Tools, FilePathBuilder, CONSTANTS\n\nclass SimilarityScore:\n @staticmethod\n def cosine_similarity(embedding_vec1, embedding_vec2):\n return 1 - scipy.spatial.distance.cosine(embedding_vec1, embedding_vec2)\n \n @staticmethod\n def jaccard_similarity(list1, list2):\n set1 = set(list1)\n set2 = set(list2)\n intersection = len(set1.intersection(set2))\n union = len(set1.union(set2))\n return float(intersection) / union\n\nclass CodeSearchWorker:\n def __init__(self, repo_embedding_lines, query_embedding_lines, output_path, sim_scorer, max_top_k, log_message):\n self.repo_embedding_lines = repo_embedding_lines # list\n self.query_embedding_lines = query_embedding_lines # list\n self.max_top_k = max_top_k\n self.sim_scorer = sim_scorer\n self.output_path = output_path\n self.log_message = log_message\n \n def _is_context_after_hole(self, repo_embedding_line, query_line):\n hole_fpath_tuple = tuple(query_line['metadata']['fpath_tuple'])\n context_is_not_after_hole = []\n for metadata in repo_embedding_line['metadata']:\n if tuple(metadata['fpath_tuple']) != hole_fpath_tuple:\n context_is_not_after_hole.append(True)\n continue\n # now we know that the repo line is in the same file as the hole\n if metadata['end_line_no'] <= query_line['metadata']['context_start_lineno']:\n context_is_not_after_hole.append(True)","source_hash":"977bc3469390b030dc2f47cd8a9b4449c9142308e58afe2b90d0fecad7359ba3","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.search_code.CodeSearchWorker","uri":"program://CodeT/class/RepoCoder.search_code.CodeSearchWorker#L27-L69","kind":"class","name":"CodeSearchWorker","path":"RepoCoder/search_code.py","language":"python","start_line":27,"end_line":69,"context_start_line":7,"context_end_line":89,"code":"import tqdm\nimport os\nimport copy\nimport functools\n\nfrom utils import Tools, FilePathBuilder, CONSTANTS\n\nclass SimilarityScore:\n @staticmethod\n def cosine_similarity(embedding_vec1, embedding_vec2):\n return 1 - scipy.spatial.distance.cosine(embedding_vec1, embedding_vec2)\n \n @staticmethod\n def jaccard_similarity(list1, list2):\n set1 = set(list1)\n set2 = set(list2)\n intersection = len(set1.intersection(set2))\n union = len(set1.union(set2))\n return float(intersection) / union\n\nclass CodeSearchWorker:\n def __init__(self, repo_embedding_lines, query_embedding_lines, output_path, sim_scorer, max_top_k, log_message):\n self.repo_embedding_lines = repo_embedding_lines # list\n self.query_embedding_lines = query_embedding_lines # list\n self.max_top_k = max_top_k\n self.sim_scorer = sim_scorer\n self.output_path = output_path\n self.log_message = log_message\n \n def _is_context_after_hole(self, repo_embedding_line, query_line):\n hole_fpath_tuple = tuple(query_line['metadata']['fpath_tuple'])\n context_is_not_after_hole = []\n for metadata in repo_embedding_line['metadata']:\n if tuple(metadata['fpath_tuple']) != hole_fpath_tuple:\n context_is_not_after_hole.append(True)\n continue\n # now we know that the repo line is in the same file as the hole\n if metadata['end_line_no'] <= query_line['metadata']['context_start_lineno']:\n context_is_not_after_hole.append(True)\n continue\n context_is_not_after_hole.append(False)\n return not any(context_is_not_after_hole)\n \n def _find_top_k_context(self, query_line):\n top_k_context = []\n query_embedding = np.array(query_line['data'][0]['embedding'])\n for repo_embedding_line in self.repo_embedding_lines:\n if self._is_context_after_hole(repo_embedding_line, query_line):\n continue\n repo_line_embedding = np.array(repo_embedding_line['data'][0]['embedding'])\n similarity_score = self.sim_scorer(query_embedding, repo_line_embedding)\n top_k_context.append((repo_embedding_line, similarity_score))\n top_k_context = sorted(top_k_context, key=lambda x: x[1], reverse=False)[-self.max_top_k:]\n return top_k_context\n\n def run(self):\n query_lines_with_retrieved_results = []\n for query_line in self.query_embedding_lines:\n new_line = copy.deepcopy(query_line)\n top_k_context = self._find_top_k_context(new_line)\n new_line['top_k_context'] = top_k_context\n query_lines_with_retrieved_results.append(new_line)\n Tools.dump_pickle(query_lines_with_retrieved_results, self.output_path)\n\n\nclass CodeSearchWrapper:\n def __init__(self, vectorizer, benchmark, repos, window_sizes, slice_sizes):\n self.vectorizer = vectorizer\n if vectorizer == 'one-gram':\n self.sim_scorer = SimilarityScore.jaccard_similarity\n self.vector_path_builder = FilePathBuilder.one_gram_vector_path\n elif vectorizer == 'ada002':\n self.sim_scorer = SimilarityScore.cosine_similarity\n self.vector_path_builder = FilePathBuilder.ada002_vector_path\n self.max_top_k = 20 # store 20 top k context for the prompt construction (top 10)\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n self.benchmark = benchmark\n \n def _run_parallel(self, query_window_path_builder, prediction_path_template=None):\n workers = []\n for window_size in self.window_sizes:","source_hash":"977bc3469390b030dc2f47cd8a9b4449c9142308e58afe2b90d0fecad7359ba3","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.search_code.CodeSearchWrapper","uri":"program://CodeT/class/RepoCoder.search_code.CodeSearchWrapper#L72-L122","kind":"class","name":"CodeSearchWrapper","path":"RepoCoder/search_code.py","language":"python","start_line":72,"end_line":122,"context_start_line":52,"context_end_line":122,"code":" query_embedding = np.array(query_line['data'][0]['embedding'])\n for repo_embedding_line in self.repo_embedding_lines:\n if self._is_context_after_hole(repo_embedding_line, query_line):\n continue\n repo_line_embedding = np.array(repo_embedding_line['data'][0]['embedding'])\n similarity_score = self.sim_scorer(query_embedding, repo_line_embedding)\n top_k_context.append((repo_embedding_line, similarity_score))\n top_k_context = sorted(top_k_context, key=lambda x: x[1], reverse=False)[-self.max_top_k:]\n return top_k_context\n\n def run(self):\n query_lines_with_retrieved_results = []\n for query_line in self.query_embedding_lines:\n new_line = copy.deepcopy(query_line)\n top_k_context = self._find_top_k_context(new_line)\n new_line['top_k_context'] = top_k_context\n query_lines_with_retrieved_results.append(new_line)\n Tools.dump_pickle(query_lines_with_retrieved_results, self.output_path)\n\n\nclass CodeSearchWrapper:\n def __init__(self, vectorizer, benchmark, repos, window_sizes, slice_sizes):\n self.vectorizer = vectorizer\n if vectorizer == 'one-gram':\n self.sim_scorer = SimilarityScore.jaccard_similarity\n self.vector_path_builder = FilePathBuilder.one_gram_vector_path\n elif vectorizer == 'ada002':\n self.sim_scorer = SimilarityScore.cosine_similarity\n self.vector_path_builder = FilePathBuilder.ada002_vector_path\n self.max_top_k = 20 # store 20 top k context for the prompt construction (top 10)\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n self.benchmark = benchmark\n \n def _run_parallel(self, query_window_path_builder, prediction_path_template=None):\n workers = []\n for window_size in self.window_sizes:\n for slice_size in self.slice_sizes:\n for repo in self.repos:\n if prediction_path_template:\n query_window_path = query_window_path_builder(\n prediction_path_template.format(window_size=window_size, slice_size=slice_size),\n repo, window_size\n )\n else:\n query_window_path = query_window_path_builder(repo, window_size)\n query_line_path = self.vector_path_builder(query_window_path)\n repo_window_path = FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n repo_embedding_path = self.vector_path_builder(repo_window_path)\n output_path = FilePathBuilder.retrieval_results_path(query_line_path, repo_embedding_path, self.max_top_k)\n repo_embedding_lines = Tools.load_pickle(repo_embedding_path)\n query_embedding_lines = Tools.load_pickle(query_line_path)\n log_message = f'repo: {repo}, window: {window_size}, slice: {slice_size} {self.vectorizer}, max_top_k: {self.max_top_k}'\n worker = CodeSearchWorker(repo_embedding_lines, query_embedding_lines, output_path, self.sim_scorer, self.max_top_k, log_message)\n workers.append(worker)\n # process pool\n with ProcessPoolExecutor(max_workers=os.cpu_count()) as executor:\n futures = {executor.submit(worker.run, ) for worker in workers}\n for future in tqdm.tqdm(as_completed(futures), total=len(futures)):\n future.result()\n\n def search_baseline_and_ground(self):\n query_line_path_temp = functools.partial(FilePathBuilder.search_first_window_path, self.benchmark, CONSTANTS.rg)\n self._run_parallel(query_line_path_temp)\n query_line_path_temp = functools.partial(FilePathBuilder.search_first_window_path, self.benchmark, CONSTANTS.gt)\n self._run_parallel(query_line_path_temp)\n \n def search_prediction(self, mode, prediction_path_template):\n query_line_path_temp = functools.partial(FilePathBuilder.gen_first_window_path, self.benchmark, mode)\n self._run_parallel(query_line_path_temp, prediction_path_template)","source_hash":"977bc3469390b030dc2f47cd8a9b4449c9142308e58afe2b90d0fecad7359ba3","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.search_code.cosine_similarity","uri":"program://CodeT/function/RepoCoder.search_code.cosine_similarity#L16-L17","kind":"function","name":"cosine_similarity","path":"RepoCoder/search_code.py","language":"python","start_line":16,"end_line":17,"context_start_line":1,"context_end_line":37,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom concurrent.futures import as_completed, ProcessPoolExecutor\nimport numpy as np\nimport scipy\nimport tqdm\nimport os\nimport copy\nimport functools\n\nfrom utils import Tools, FilePathBuilder, CONSTANTS\n\nclass SimilarityScore:\n @staticmethod\n def cosine_similarity(embedding_vec1, embedding_vec2):\n return 1 - scipy.spatial.distance.cosine(embedding_vec1, embedding_vec2)\n \n @staticmethod\n def jaccard_similarity(list1, list2):\n set1 = set(list1)\n set2 = set(list2)\n intersection = len(set1.intersection(set2))\n union = len(set1.union(set2))\n return float(intersection) / union\n\nclass CodeSearchWorker:\n def __init__(self, repo_embedding_lines, query_embedding_lines, output_path, sim_scorer, max_top_k, log_message):\n self.repo_embedding_lines = repo_embedding_lines # list\n self.query_embedding_lines = query_embedding_lines # list\n self.max_top_k = max_top_k\n self.sim_scorer = sim_scorer\n self.output_path = output_path\n self.log_message = log_message\n \n def _is_context_after_hole(self, repo_embedding_line, query_line):\n hole_fpath_tuple = tuple(query_line['metadata']['fpath_tuple'])","source_hash":"977bc3469390b030dc2f47cd8a9b4449c9142308e58afe2b90d0fecad7359ba3","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.search_code.jaccard_similarity","uri":"program://CodeT/function/RepoCoder.search_code.jaccard_similarity#L20-L25","kind":"function","name":"jaccard_similarity","path":"RepoCoder/search_code.py","language":"python","start_line":20,"end_line":25,"context_start_line":1,"context_end_line":45,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom concurrent.futures import as_completed, ProcessPoolExecutor\nimport numpy as np\nimport scipy\nimport tqdm\nimport os\nimport copy\nimport functools\n\nfrom utils import Tools, FilePathBuilder, CONSTANTS\n\nclass SimilarityScore:\n @staticmethod\n def cosine_similarity(embedding_vec1, embedding_vec2):\n return 1 - scipy.spatial.distance.cosine(embedding_vec1, embedding_vec2)\n \n @staticmethod\n def jaccard_similarity(list1, list2):\n set1 = set(list1)\n set2 = set(list2)\n intersection = len(set1.intersection(set2))\n union = len(set1.union(set2))\n return float(intersection) / union\n\nclass CodeSearchWorker:\n def __init__(self, repo_embedding_lines, query_embedding_lines, output_path, sim_scorer, max_top_k, log_message):\n self.repo_embedding_lines = repo_embedding_lines # list\n self.query_embedding_lines = query_embedding_lines # list\n self.max_top_k = max_top_k\n self.sim_scorer = sim_scorer\n self.output_path = output_path\n self.log_message = log_message\n \n def _is_context_after_hole(self, repo_embedding_line, query_line):\n hole_fpath_tuple = tuple(query_line['metadata']['fpath_tuple'])\n context_is_not_after_hole = []\n for metadata in repo_embedding_line['metadata']:\n if tuple(metadata['fpath_tuple']) != hole_fpath_tuple:\n context_is_not_after_hole.append(True)\n continue\n # now we know that the repo line is in the same file as the hole\n if metadata['end_line_no'] <= query_line['metadata']['context_start_lineno']:\n context_is_not_after_hole.append(True)","source_hash":"977bc3469390b030dc2f47cd8a9b4449c9142308e58afe2b90d0fecad7359ba3","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.search_code.__init__","uri":"program://CodeT/function/RepoCoder.search_code.__init__#L73-L85","kind":"function","name":"__init__","path":"RepoCoder/search_code.py","language":"python","start_line":73,"end_line":85,"context_start_line":53,"context_end_line":105,"code":" for repo_embedding_line in self.repo_embedding_lines:\n if self._is_context_after_hole(repo_embedding_line, query_line):\n continue\n repo_line_embedding = np.array(repo_embedding_line['data'][0]['embedding'])\n similarity_score = self.sim_scorer(query_embedding, repo_line_embedding)\n top_k_context.append((repo_embedding_line, similarity_score))\n top_k_context = sorted(top_k_context, key=lambda x: x[1], reverse=False)[-self.max_top_k:]\n return top_k_context\n\n def run(self):\n query_lines_with_retrieved_results = []\n for query_line in self.query_embedding_lines:\n new_line = copy.deepcopy(query_line)\n top_k_context = self._find_top_k_context(new_line)\n new_line['top_k_context'] = top_k_context\n query_lines_with_retrieved_results.append(new_line)\n Tools.dump_pickle(query_lines_with_retrieved_results, self.output_path)\n\n\nclass CodeSearchWrapper:\n def __init__(self, vectorizer, benchmark, repos, window_sizes, slice_sizes):\n self.vectorizer = vectorizer\n if vectorizer == 'one-gram':\n self.sim_scorer = SimilarityScore.jaccard_similarity\n self.vector_path_builder = FilePathBuilder.one_gram_vector_path\n elif vectorizer == 'ada002':\n self.sim_scorer = SimilarityScore.cosine_similarity\n self.vector_path_builder = FilePathBuilder.ada002_vector_path\n self.max_top_k = 20 # store 20 top k context for the prompt construction (top 10)\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n self.benchmark = benchmark\n \n def _run_parallel(self, query_window_path_builder, prediction_path_template=None):\n workers = []\n for window_size in self.window_sizes:\n for slice_size in self.slice_sizes:\n for repo in self.repos:\n if prediction_path_template:\n query_window_path = query_window_path_builder(\n prediction_path_template.format(window_size=window_size, slice_size=slice_size),\n repo, window_size\n )\n else:\n query_window_path = query_window_path_builder(repo, window_size)\n query_line_path = self.vector_path_builder(query_window_path)\n repo_window_path = FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n repo_embedding_path = self.vector_path_builder(repo_window_path)\n output_path = FilePathBuilder.retrieval_results_path(query_line_path, repo_embedding_path, self.max_top_k)\n repo_embedding_lines = Tools.load_pickle(repo_embedding_path)\n query_embedding_lines = Tools.load_pickle(query_line_path)\n log_message = f'repo: {repo}, window: {window_size}, slice: {slice_size} {self.vectorizer}, max_top_k: {self.max_top_k}'","source_hash":"977bc3469390b030dc2f47cd8a9b4449c9142308e58afe2b90d0fecad7359ba3","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.search_code._is_context_after_hole","uri":"program://CodeT/function/RepoCoder.search_code._is_context_after_hole#L36-L48","kind":"function","name":"_is_context_after_hole","path":"RepoCoder/search_code.py","language":"python","start_line":36,"end_line":48,"context_start_line":16,"context_end_line":68,"code":" def cosine_similarity(embedding_vec1, embedding_vec2):\n return 1 - scipy.spatial.distance.cosine(embedding_vec1, embedding_vec2)\n \n @staticmethod\n def jaccard_similarity(list1, list2):\n set1 = set(list1)\n set2 = set(list2)\n intersection = len(set1.intersection(set2))\n union = len(set1.union(set2))\n return float(intersection) / union\n\nclass CodeSearchWorker:\n def __init__(self, repo_embedding_lines, query_embedding_lines, output_path, sim_scorer, max_top_k, log_message):\n self.repo_embedding_lines = repo_embedding_lines # list\n self.query_embedding_lines = query_embedding_lines # list\n self.max_top_k = max_top_k\n self.sim_scorer = sim_scorer\n self.output_path = output_path\n self.log_message = log_message\n \n def _is_context_after_hole(self, repo_embedding_line, query_line):\n hole_fpath_tuple = tuple(query_line['metadata']['fpath_tuple'])\n context_is_not_after_hole = []\n for metadata in repo_embedding_line['metadata']:\n if tuple(metadata['fpath_tuple']) != hole_fpath_tuple:\n context_is_not_after_hole.append(True)\n continue\n # now we know that the repo line is in the same file as the hole\n if metadata['end_line_no'] <= query_line['metadata']['context_start_lineno']:\n context_is_not_after_hole.append(True)\n continue\n context_is_not_after_hole.append(False)\n return not any(context_is_not_after_hole)\n \n def _find_top_k_context(self, query_line):\n top_k_context = []\n query_embedding = np.array(query_line['data'][0]['embedding'])\n for repo_embedding_line in self.repo_embedding_lines:\n if self._is_context_after_hole(repo_embedding_line, query_line):\n continue\n repo_line_embedding = np.array(repo_embedding_line['data'][0]['embedding'])\n similarity_score = self.sim_scorer(query_embedding, repo_line_embedding)\n top_k_context.append((repo_embedding_line, similarity_score))\n top_k_context = sorted(top_k_context, key=lambda x: x[1], reverse=False)[-self.max_top_k:]\n return top_k_context\n\n def run(self):\n query_lines_with_retrieved_results = []\n for query_line in self.query_embedding_lines:\n new_line = copy.deepcopy(query_line)\n top_k_context = self._find_top_k_context(new_line)\n new_line['top_k_context'] = top_k_context\n query_lines_with_retrieved_results.append(new_line)","source_hash":"977bc3469390b030dc2f47cd8a9b4449c9142308e58afe2b90d0fecad7359ba3","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.search_code._find_top_k_context","uri":"program://CodeT/function/RepoCoder.search_code._find_top_k_context#L50-L60","kind":"function","name":"_find_top_k_context","path":"RepoCoder/search_code.py","language":"python","start_line":50,"end_line":60,"context_start_line":30,"context_end_line":80,"code":" self.query_embedding_lines = query_embedding_lines # list\n self.max_top_k = max_top_k\n self.sim_scorer = sim_scorer\n self.output_path = output_path\n self.log_message = log_message\n \n def _is_context_after_hole(self, repo_embedding_line, query_line):\n hole_fpath_tuple = tuple(query_line['metadata']['fpath_tuple'])\n context_is_not_after_hole = []\n for metadata in repo_embedding_line['metadata']:\n if tuple(metadata['fpath_tuple']) != hole_fpath_tuple:\n context_is_not_after_hole.append(True)\n continue\n # now we know that the repo line is in the same file as the hole\n if metadata['end_line_no'] <= query_line['metadata']['context_start_lineno']:\n context_is_not_after_hole.append(True)\n continue\n context_is_not_after_hole.append(False)\n return not any(context_is_not_after_hole)\n \n def _find_top_k_context(self, query_line):\n top_k_context = []\n query_embedding = np.array(query_line['data'][0]['embedding'])\n for repo_embedding_line in self.repo_embedding_lines:\n if self._is_context_after_hole(repo_embedding_line, query_line):\n continue\n repo_line_embedding = np.array(repo_embedding_line['data'][0]['embedding'])\n similarity_score = self.sim_scorer(query_embedding, repo_line_embedding)\n top_k_context.append((repo_embedding_line, similarity_score))\n top_k_context = sorted(top_k_context, key=lambda x: x[1], reverse=False)[-self.max_top_k:]\n return top_k_context\n\n def run(self):\n query_lines_with_retrieved_results = []\n for query_line in self.query_embedding_lines:\n new_line = copy.deepcopy(query_line)\n top_k_context = self._find_top_k_context(new_line)\n new_line['top_k_context'] = top_k_context\n query_lines_with_retrieved_results.append(new_line)\n Tools.dump_pickle(query_lines_with_retrieved_results, self.output_path)\n\n\nclass CodeSearchWrapper:\n def __init__(self, vectorizer, benchmark, repos, window_sizes, slice_sizes):\n self.vectorizer = vectorizer\n if vectorizer == 'one-gram':\n self.sim_scorer = SimilarityScore.jaccard_similarity\n self.vector_path_builder = FilePathBuilder.one_gram_vector_path\n elif vectorizer == 'ada002':\n self.sim_scorer = SimilarityScore.cosine_similarity\n self.vector_path_builder = FilePathBuilder.ada002_vector_path","source_hash":"977bc3469390b030dc2f47cd8a9b4449c9142308e58afe2b90d0fecad7359ba3","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.search_code.run","uri":"program://CodeT/function/RepoCoder.search_code.run#L62-L69","kind":"function","name":"run","path":"RepoCoder/search_code.py","language":"python","start_line":62,"end_line":69,"context_start_line":42,"context_end_line":89,"code":" continue\n # now we know that the repo line is in the same file as the hole\n if metadata['end_line_no'] <= query_line['metadata']['context_start_lineno']:\n context_is_not_after_hole.append(True)\n continue\n context_is_not_after_hole.append(False)\n return not any(context_is_not_after_hole)\n \n def _find_top_k_context(self, query_line):\n top_k_context = []\n query_embedding = np.array(query_line['data'][0]['embedding'])\n for repo_embedding_line in self.repo_embedding_lines:\n if self._is_context_after_hole(repo_embedding_line, query_line):\n continue\n repo_line_embedding = np.array(repo_embedding_line['data'][0]['embedding'])\n similarity_score = self.sim_scorer(query_embedding, repo_line_embedding)\n top_k_context.append((repo_embedding_line, similarity_score))\n top_k_context = sorted(top_k_context, key=lambda x: x[1], reverse=False)[-self.max_top_k:]\n return top_k_context\n\n def run(self):\n query_lines_with_retrieved_results = []\n for query_line in self.query_embedding_lines:\n new_line = copy.deepcopy(query_line)\n top_k_context = self._find_top_k_context(new_line)\n new_line['top_k_context'] = top_k_context\n query_lines_with_retrieved_results.append(new_line)\n Tools.dump_pickle(query_lines_with_retrieved_results, self.output_path)\n\n\nclass CodeSearchWrapper:\n def __init__(self, vectorizer, benchmark, repos, window_sizes, slice_sizes):\n self.vectorizer = vectorizer\n if vectorizer == 'one-gram':\n self.sim_scorer = SimilarityScore.jaccard_similarity\n self.vector_path_builder = FilePathBuilder.one_gram_vector_path\n elif vectorizer == 'ada002':\n self.sim_scorer = SimilarityScore.cosine_similarity\n self.vector_path_builder = FilePathBuilder.ada002_vector_path\n self.max_top_k = 20 # store 20 top k context for the prompt construction (top 10)\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n self.benchmark = benchmark\n \n def _run_parallel(self, query_window_path_builder, prediction_path_template=None):\n workers = []\n for window_size in self.window_sizes:","source_hash":"977bc3469390b030dc2f47cd8a9b4449c9142308e58afe2b90d0fecad7359ba3","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.search_code._run_parallel","uri":"program://CodeT/function/RepoCoder.search_code._run_parallel#L87-L112","kind":"function","name":"_run_parallel","path":"RepoCoder/search_code.py","language":"python","start_line":87,"end_line":112,"context_start_line":67,"context_end_line":122,"code":" new_line['top_k_context'] = top_k_context\n query_lines_with_retrieved_results.append(new_line)\n Tools.dump_pickle(query_lines_with_retrieved_results, self.output_path)\n\n\nclass CodeSearchWrapper:\n def __init__(self, vectorizer, benchmark, repos, window_sizes, slice_sizes):\n self.vectorizer = vectorizer\n if vectorizer == 'one-gram':\n self.sim_scorer = SimilarityScore.jaccard_similarity\n self.vector_path_builder = FilePathBuilder.one_gram_vector_path\n elif vectorizer == 'ada002':\n self.sim_scorer = SimilarityScore.cosine_similarity\n self.vector_path_builder = FilePathBuilder.ada002_vector_path\n self.max_top_k = 20 # store 20 top k context for the prompt construction (top 10)\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n self.benchmark = benchmark\n \n def _run_parallel(self, query_window_path_builder, prediction_path_template=None):\n workers = []\n for window_size in self.window_sizes:\n for slice_size in self.slice_sizes:\n for repo in self.repos:\n if prediction_path_template:\n query_window_path = query_window_path_builder(\n prediction_path_template.format(window_size=window_size, slice_size=slice_size),\n repo, window_size\n )\n else:\n query_window_path = query_window_path_builder(repo, window_size)\n query_line_path = self.vector_path_builder(query_window_path)\n repo_window_path = FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n repo_embedding_path = self.vector_path_builder(repo_window_path)\n output_path = FilePathBuilder.retrieval_results_path(query_line_path, repo_embedding_path, self.max_top_k)\n repo_embedding_lines = Tools.load_pickle(repo_embedding_path)\n query_embedding_lines = Tools.load_pickle(query_line_path)\n log_message = f'repo: {repo}, window: {window_size}, slice: {slice_size} {self.vectorizer}, max_top_k: {self.max_top_k}'\n worker = CodeSearchWorker(repo_embedding_lines, query_embedding_lines, output_path, self.sim_scorer, self.max_top_k, log_message)\n workers.append(worker)\n # process pool\n with ProcessPoolExecutor(max_workers=os.cpu_count()) as executor:\n futures = {executor.submit(worker.run, ) for worker in workers}\n for future in tqdm.tqdm(as_completed(futures), total=len(futures)):\n future.result()\n\n def search_baseline_and_ground(self):\n query_line_path_temp = functools.partial(FilePathBuilder.search_first_window_path, self.benchmark, CONSTANTS.rg)\n self._run_parallel(query_line_path_temp)\n query_line_path_temp = functools.partial(FilePathBuilder.search_first_window_path, self.benchmark, CONSTANTS.gt)\n self._run_parallel(query_line_path_temp)\n \n def search_prediction(self, mode, prediction_path_template):\n query_line_path_temp = functools.partial(FilePathBuilder.gen_first_window_path, self.benchmark, mode)\n self._run_parallel(query_line_path_temp, prediction_path_template)","source_hash":"977bc3469390b030dc2f47cd8a9b4449c9142308e58afe2b90d0fecad7359ba3","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.search_code.search_baseline_and_ground","uri":"program://CodeT/function/RepoCoder.search_code.search_baseline_and_ground#L114-L118","kind":"function","name":"search_baseline_and_ground","path":"RepoCoder/search_code.py","language":"python","start_line":114,"end_line":118,"context_start_line":94,"context_end_line":122,"code":" prediction_path_template.format(window_size=window_size, slice_size=slice_size),\n repo, window_size\n )\n else:\n query_window_path = query_window_path_builder(repo, window_size)\n query_line_path = self.vector_path_builder(query_window_path)\n repo_window_path = FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n repo_embedding_path = self.vector_path_builder(repo_window_path)\n output_path = FilePathBuilder.retrieval_results_path(query_line_path, repo_embedding_path, self.max_top_k)\n repo_embedding_lines = Tools.load_pickle(repo_embedding_path)\n query_embedding_lines = Tools.load_pickle(query_line_path)\n log_message = f'repo: {repo}, window: {window_size}, slice: {slice_size} {self.vectorizer}, max_top_k: {self.max_top_k}'\n worker = CodeSearchWorker(repo_embedding_lines, query_embedding_lines, output_path, self.sim_scorer, self.max_top_k, log_message)\n workers.append(worker)\n # process pool\n with ProcessPoolExecutor(max_workers=os.cpu_count()) as executor:\n futures = {executor.submit(worker.run, ) for worker in workers}\n for future in tqdm.tqdm(as_completed(futures), total=len(futures)):\n future.result()\n\n def search_baseline_and_ground(self):\n query_line_path_temp = functools.partial(FilePathBuilder.search_first_window_path, self.benchmark, CONSTANTS.rg)\n self._run_parallel(query_line_path_temp)\n query_line_path_temp = functools.partial(FilePathBuilder.search_first_window_path, self.benchmark, CONSTANTS.gt)\n self._run_parallel(query_line_path_temp)\n \n def search_prediction(self, mode, prediction_path_template):\n query_line_path_temp = functools.partial(FilePathBuilder.gen_first_window_path, self.benchmark, mode)\n self._run_parallel(query_line_path_temp, prediction_path_template)","source_hash":"977bc3469390b030dc2f47cd8a9b4449c9142308e58afe2b90d0fecad7359ba3","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.search_code.search_prediction","uri":"program://CodeT/function/RepoCoder.search_code.search_prediction#L120-L122","kind":"function","name":"search_prediction","path":"RepoCoder/search_code.py","language":"python","start_line":120,"end_line":122,"context_start_line":100,"context_end_line":122,"code":" repo_window_path = FilePathBuilder.repo_windows_path(repo, window_size, slice_size)\n repo_embedding_path = self.vector_path_builder(repo_window_path)\n output_path = FilePathBuilder.retrieval_results_path(query_line_path, repo_embedding_path, self.max_top_k)\n repo_embedding_lines = Tools.load_pickle(repo_embedding_path)\n query_embedding_lines = Tools.load_pickle(query_line_path)\n log_message = f'repo: {repo}, window: {window_size}, slice: {slice_size} {self.vectorizer}, max_top_k: {self.max_top_k}'\n worker = CodeSearchWorker(repo_embedding_lines, query_embedding_lines, output_path, self.sim_scorer, self.max_top_k, log_message)\n workers.append(worker)\n # process pool\n with ProcessPoolExecutor(max_workers=os.cpu_count()) as executor:\n futures = {executor.submit(worker.run, ) for worker in workers}\n for future in tqdm.tqdm(as_completed(futures), total=len(futures)):\n future.result()\n\n def search_baseline_and_ground(self):\n query_line_path_temp = functools.partial(FilePathBuilder.search_first_window_path, self.benchmark, CONSTANTS.rg)\n self._run_parallel(query_line_path_temp)\n query_line_path_temp = functools.partial(FilePathBuilder.search_first_window_path, self.benchmark, CONSTANTS.gt)\n self._run_parallel(query_line_path_temp)\n \n def search_prediction(self, mode, prediction_path_template):\n query_line_path_temp = functools.partial(FilePathBuilder.gen_first_window_path, self.benchmark, mode)\n self._run_parallel(query_line_path_temp, prediction_path_template)","source_hash":"977bc3469390b030dc2f47cd8a9b4449c9142308e58afe2b90d0fecad7359ba3","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.run_pipeline","uri":"program://CodeT/module/RepoCoder.run_pipeline#L1-L70","kind":"module","name":"RepoCoder.run_pipeline","path":"RepoCoder/run_pipeline.py","language":"python","start_line":1,"end_line":70,"context_start_line":1,"context_end_line":70,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport os\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n\nfrom make_window import MakeWindowWrapper\nfrom build_vector import BuildVectorWrapper, BagOfWords\nfrom search_code import CodeSearchWrapper\nfrom build_prompt import BuildPromptWrapper\n\nfrom utils import CONSTANTS, CodexTokenizer\n\ndef make_repo_window(repos, window_sizes, slice_sizes):\n worker = MakeWindowWrapper(None, repos, window_sizes, slice_sizes)\n worker.window_for_repo_files()\n\n\ndef run_RG1_and_oracle_method(benchmark, repos, window_sizes, slice_sizes):\n # build code snippets for all the repositories\n make_repo_window(repos, window_sizes, slice_sizes)\n # build code snippets for vanilla retrieval-augmented approach and ground truth\n MakeWindowWrapper(benchmark, repos, window_sizes, slice_sizes).window_for_baseline_and_ground()\n # build vector for vanilla retrieval-augmented approach and ground truth\n vectorizer = BagOfWords\n BuildVectorWrapper(benchmark, vectorizer, repos, window_sizes, slice_sizes).vectorize_baseline_and_ground_windows()\n # search code for vanilla retrieval-augmented approach and ground truth\n CodeSearchWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes).search_baseline_and_ground()\n # build prompt for vanilla retrieval-augmented approach and ground truth\n tokenizer = CodexTokenizer\n mode = CONSTANTS.rg\n output_file_path = 'prompts/rg-one-gram-ws-20-ss-2.jsonl'\n BuildPromptWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes, tokenizer).build_first_search_prompt(mode, output_file_path)\n\n mode = CONSTANTS.gt\n output_file_path = 'prompts/gt-one-gram-ws-20-ss-2.jsonl'\n BuildPromptWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes, tokenizer).build_first_search_prompt(mode, output_file_path)\n\n\ndef run_RepoCoder_method(benchmark, repos, window_sizes, slice_sizes, prediction_path):\n mode = CONSTANTS.rgrg\n MakeWindowWrapper(benchmark, repos, window_sizes, slice_sizes).window_for_prediction(mode, prediction_path)\n vectorizer = BagOfWords\n BuildVectorWrapper(benchmark, vectorizer, repos, window_sizes, slice_sizes).vectorize_prediction_windows(mode, prediction_path)\n CodeSearchWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes).search_prediction(mode, prediction_path)\n tokenizer = CodexTokenizer\n output_file_path = 'prompts/repocoder-one-gram-ws-20-ss-2.jsonl'\n BuildPromptWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes, tokenizer).build_prediction_prompt(mode, prediction_path, output_file_path)\n\n\nif __name__ == '__main__':\n repos = [\n 'huggingface_diffusers',\n 'nerfstudio-project_nerfstudio',\n 'awslabs_fortuna',\n 'huggingface_evaluate',\n 'google_vizier',\n 'alibaba_FederatedScope',\n 'pytorch_rl',\n 'opendilab_ACE',\n ]\n window_sizes = [20]\n slice_sizes = [2] # 20 / 2 = 10\n\n # build prompt for the RG1 and oracle methods\n run_RG1_and_oracle_method(CONSTANTS.api_benchmark, repos, window_sizes, slice_sizes)\n\n # build prompt for the RepoCoder method\n prediction_path = 'predictions/rg-one-gram-ws-20-ss-2_samples.0.jsonl'\n run_RepoCoder_method(CONSTANTS.api_benchmark, repos, window_sizes, slice_sizes, prediction_path)","source_hash":"b08804b7d4de453ee1d0cdc8333c0658608df3b67a4a298bf447421f08da7bf6","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.run_pipeline.make_repo_window","uri":"program://CodeT/function/RepoCoder.run_pipeline.make_repo_window#L14-L16","kind":"function","name":"make_repo_window","path":"RepoCoder/run_pipeline.py","language":"python","start_line":14,"end_line":16,"context_start_line":1,"context_end_line":36,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport os\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n\nfrom make_window import MakeWindowWrapper\nfrom build_vector import BuildVectorWrapper, BagOfWords\nfrom search_code import CodeSearchWrapper\nfrom build_prompt import BuildPromptWrapper\n\nfrom utils import CONSTANTS, CodexTokenizer\n\ndef make_repo_window(repos, window_sizes, slice_sizes):\n worker = MakeWindowWrapper(None, repos, window_sizes, slice_sizes)\n worker.window_for_repo_files()\n\n\ndef run_RG1_and_oracle_method(benchmark, repos, window_sizes, slice_sizes):\n # build code snippets for all the repositories\n make_repo_window(repos, window_sizes, slice_sizes)\n # build code snippets for vanilla retrieval-augmented approach and ground truth\n MakeWindowWrapper(benchmark, repos, window_sizes, slice_sizes).window_for_baseline_and_ground()\n # build vector for vanilla retrieval-augmented approach and ground truth\n vectorizer = BagOfWords\n BuildVectorWrapper(benchmark, vectorizer, repos, window_sizes, slice_sizes).vectorize_baseline_and_ground_windows()\n # search code for vanilla retrieval-augmented approach and ground truth\n CodeSearchWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes).search_baseline_and_ground()\n # build prompt for vanilla retrieval-augmented approach and ground truth\n tokenizer = CodexTokenizer\n mode = CONSTANTS.rg\n output_file_path = 'prompts/rg-one-gram-ws-20-ss-2.jsonl'\n BuildPromptWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes, tokenizer).build_first_search_prompt(mode, output_file_path)\n\n mode = CONSTANTS.gt\n output_file_path = 'prompts/gt-one-gram-ws-20-ss-2.jsonl'","source_hash":"b08804b7d4de453ee1d0cdc8333c0658608df3b67a4a298bf447421f08da7bf6","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.run_pipeline.run_RG1_and_oracle_method","uri":"program://CodeT/function/RepoCoder.run_pipeline.run_RG1_and_oracle_method#L19-L37","kind":"function","name":"run_RG1_and_oracle_method","path":"RepoCoder/run_pipeline.py","language":"python","start_line":19,"end_line":37,"context_start_line":1,"context_end_line":57,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport os\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n\nfrom make_window import MakeWindowWrapper\nfrom build_vector import BuildVectorWrapper, BagOfWords\nfrom search_code import CodeSearchWrapper\nfrom build_prompt import BuildPromptWrapper\n\nfrom utils import CONSTANTS, CodexTokenizer\n\ndef make_repo_window(repos, window_sizes, slice_sizes):\n worker = MakeWindowWrapper(None, repos, window_sizes, slice_sizes)\n worker.window_for_repo_files()\n\n\ndef run_RG1_and_oracle_method(benchmark, repos, window_sizes, slice_sizes):\n # build code snippets for all the repositories\n make_repo_window(repos, window_sizes, slice_sizes)\n # build code snippets for vanilla retrieval-augmented approach and ground truth\n MakeWindowWrapper(benchmark, repos, window_sizes, slice_sizes).window_for_baseline_and_ground()\n # build vector for vanilla retrieval-augmented approach and ground truth\n vectorizer = BagOfWords\n BuildVectorWrapper(benchmark, vectorizer, repos, window_sizes, slice_sizes).vectorize_baseline_and_ground_windows()\n # search code for vanilla retrieval-augmented approach and ground truth\n CodeSearchWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes).search_baseline_and_ground()\n # build prompt for vanilla retrieval-augmented approach and ground truth\n tokenizer = CodexTokenizer\n mode = CONSTANTS.rg\n output_file_path = 'prompts/rg-one-gram-ws-20-ss-2.jsonl'\n BuildPromptWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes, tokenizer).build_first_search_prompt(mode, output_file_path)\n\n mode = CONSTANTS.gt\n output_file_path = 'prompts/gt-one-gram-ws-20-ss-2.jsonl'\n BuildPromptWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes, tokenizer).build_first_search_prompt(mode, output_file_path)\n\n\ndef run_RepoCoder_method(benchmark, repos, window_sizes, slice_sizes, prediction_path):\n mode = CONSTANTS.rgrg\n MakeWindowWrapper(benchmark, repos, window_sizes, slice_sizes).window_for_prediction(mode, prediction_path)\n vectorizer = BagOfWords\n BuildVectorWrapper(benchmark, vectorizer, repos, window_sizes, slice_sizes).vectorize_prediction_windows(mode, prediction_path)\n CodeSearchWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes).search_prediction(mode, prediction_path)\n tokenizer = CodexTokenizer\n output_file_path = 'prompts/repocoder-one-gram-ws-20-ss-2.jsonl'\n BuildPromptWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes, tokenizer).build_prediction_prompt(mode, prediction_path, output_file_path)\n\n\nif __name__ == '__main__':\n repos = [\n 'huggingface_diffusers',\n 'nerfstudio-project_nerfstudio',\n 'awslabs_fortuna',\n 'huggingface_evaluate',\n 'google_vizier',","source_hash":"b08804b7d4de453ee1d0cdc8333c0658608df3b67a4a298bf447421f08da7bf6","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.run_pipeline.run_RepoCoder_method","uri":"program://CodeT/function/RepoCoder.run_pipeline.run_RepoCoder_method#L40-L48","kind":"function","name":"run_RepoCoder_method","path":"RepoCoder/run_pipeline.py","language":"python","start_line":40,"end_line":48,"context_start_line":20,"context_end_line":68,"code":" # build code snippets for all the repositories\n make_repo_window(repos, window_sizes, slice_sizes)\n # build code snippets for vanilla retrieval-augmented approach and ground truth\n MakeWindowWrapper(benchmark, repos, window_sizes, slice_sizes).window_for_baseline_and_ground()\n # build vector for vanilla retrieval-augmented approach and ground truth\n vectorizer = BagOfWords\n BuildVectorWrapper(benchmark, vectorizer, repos, window_sizes, slice_sizes).vectorize_baseline_and_ground_windows()\n # search code for vanilla retrieval-augmented approach and ground truth\n CodeSearchWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes).search_baseline_and_ground()\n # build prompt for vanilla retrieval-augmented approach and ground truth\n tokenizer = CodexTokenizer\n mode = CONSTANTS.rg\n output_file_path = 'prompts/rg-one-gram-ws-20-ss-2.jsonl'\n BuildPromptWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes, tokenizer).build_first_search_prompt(mode, output_file_path)\n\n mode = CONSTANTS.gt\n output_file_path = 'prompts/gt-one-gram-ws-20-ss-2.jsonl'\n BuildPromptWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes, tokenizer).build_first_search_prompt(mode, output_file_path)\n\n\ndef run_RepoCoder_method(benchmark, repos, window_sizes, slice_sizes, prediction_path):\n mode = CONSTANTS.rgrg\n MakeWindowWrapper(benchmark, repos, window_sizes, slice_sizes).window_for_prediction(mode, prediction_path)\n vectorizer = BagOfWords\n BuildVectorWrapper(benchmark, vectorizer, repos, window_sizes, slice_sizes).vectorize_prediction_windows(mode, prediction_path)\n CodeSearchWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes).search_prediction(mode, prediction_path)\n tokenizer = CodexTokenizer\n output_file_path = 'prompts/repocoder-one-gram-ws-20-ss-2.jsonl'\n BuildPromptWrapper('one-gram', benchmark, repos, window_sizes, slice_sizes, tokenizer).build_prediction_prompt(mode, prediction_path, output_file_path)\n\n\nif __name__ == '__main__':\n repos = [\n 'huggingface_diffusers',\n 'nerfstudio-project_nerfstudio',\n 'awslabs_fortuna',\n 'huggingface_evaluate',\n 'google_vizier',\n 'alibaba_FederatedScope',\n 'pytorch_rl',\n 'opendilab_ACE',\n ]\n window_sizes = [20]\n slice_sizes = [2] # 20 / 2 = 10\n\n # build prompt for the RG1 and oracle methods\n run_RG1_and_oracle_method(CONSTANTS.api_benchmark, repos, window_sizes, slice_sizes)\n\n # build prompt for the RepoCoder method","source_hash":"b08804b7d4de453ee1d0cdc8333c0658608df3b67a4a298bf447421f08da7bf6","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils","uri":"program://CodeT/module/RepoCoder.utils#L1-L168","kind":"module","name":"RepoCoder.utils","path":"RepoCoder/utils.py","language":"python","start_line":1,"end_line":168,"context_start_line":1,"context_end_line":168,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport os\nimport glob\nimport pickle\nimport json\nimport tiktoken\nfrom transformers import AutoTokenizer\n\nclass CONSTANTS:\n # regular version for Codex\n api_benchmark = 'random_api'\n line_benchmark = 'random_line'\n # short version for CodeGen\n short_api_benchmark = 'short_api'\n short_line_benchmark = 'short_line'\n gt = 'gt'\n rg = 'r-g' # vanilla retrieval-augmented approach\n rgrg = 'r-g-r-g' # RepoCoder, two-stage retrieval and generation\n\nclass FilePathBuilder:\n api_completion_benchmark = 'datasets/random-api-completion.test.jsonl'\n random_line_completion_benchmark = 'datasets/random-line-completion.test.jsonl'\n # short version for codegen\n short_api_completion_benchmark = 'datasets/random-api-completion-short-version.test.jsonl'\n short_random_line_completion_benchmark = 'datasets/random-line-completion-short-version.test.jsonl'\n repo_base_dir = 'repositories/line_and_api_level'\n\n @staticmethod\n def make_needed_dir(file_path):\n dir_path = os.path.dirname(file_path)\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)\n\n @staticmethod\n def repo_windows_path(repo, window_size, slice_size):\n out_path = os.path.join('cache/window/repos', f'{repo}_ws{window_size}_slice{slice_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n \n @staticmethod\n def search_first_window_path(benchmark, mode, repo, window_size):\n # mode includes gt and s-g\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def gen_first_window_path(benchmark, mode, prediction_path, repo, window_size):\n prediction_file_name = os.path.basename(prediction_path).replace('.0.jsonl', '')\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{prediction_file_name}.{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def one_gram_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.one-gram.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def ada002_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.ada002.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def retrieval_results_path(query_vector_file, repo_vector_file, max_top_k):\n retrieval_base_dir = os.path.dirname(query_vector_file.replace('/vector/', '/retrieval/'))\n query_file_name = os.path.basename(query_vector_file)\n if query_file_name.endswith('.one-gram.pkl'):\n query_file_name = query_file_name[:-len('.one-gram.pkl')]\n elif query_file_name.endswith('.ada002.pkl'):\n query_file_name = query_file_name[:-len('.ada002.pkl')]\n repo_file_name = os.path.basename(repo_vector_file)[:-len('.pkl')]\n out_path = os.path.join(retrieval_base_dir, f'{query_file_name}.{repo_file_name}.top{max_top_k}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n\nclass CodexTokenizer:\n def __init__(self):\n self.tokenizer = tiktoken.get_encoding(\"p50k_base\")\n \n def tokenize(self, text):\n # return self.tokenizer.encode(text)\n return self.tokenizer.encode_ordinary(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass CodeGenTokenizer:\n def __init__(self):\n self.tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-6B-mono')\n\n def tokenize(self, text):\n return self.tokenizer.encode(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass Tools:\n @staticmethod\n def read_code(fname):\n with open(fname, 'r', encoding='utf8') as f:\n return f.read()\n \n @staticmethod\n def load_pickle(fname):\n with open(fname, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def dump_pickle(obj, fname):\n with open(fname, 'wb') as f:\n pickle.dump(obj, f)\n \n @staticmethod\n def dump_json(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n json.dump(obj, f)\n\n @staticmethod\n def dump_jsonl(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n for item in obj:\n f.write(json.dumps(item) + '\\n')\n \n @staticmethod\n def load_jsonl(fname):\n with open(fname, 'r', encoding='utf8') as f:\n lines = []\n for line in f:\n lines.append(json.loads(line))\n return lines\n \n @staticmethod\n def iterate_repository(repo):\n base_dir = FilePathBuilder.repo_base_dir\n pattern = os.path.join(f'{base_dir}/{repo}', \"**\", \"*.py\")\n files = glob.glob(pattern, recursive=True)\n\n skipped_files = []\n loaded_code_files = dict()\n base_dir_list = os.path.normpath(base_dir).split(os.sep)\n for fname in files:\n try:\n code = Tools.read_code(fname)\n fpath_tuple = tuple(os.path.normpath(fname).split(os.sep)[len(base_dir_list):])\n loaded_code_files[fpath_tuple]= code\n except Exception as e:\n skipped_files.append((fname, e))\n continue\n\n if len(skipped_files) > 0:\n print(f\"Skipped {len(skipped_files)} out of {len(files)} files due to I/O errors\")\n for fname, e in skipped_files:\n print(f\"{fname}: {e}\")\n return loaded_code_files\n\n @staticmethod\n def tokenize(code):\n tokenizer = CodexTokenizer()\n return tokenizer.tokenize(code)","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.CONSTANTS","uri":"program://CodeT/class/RepoCoder.utils.CONSTANTS#L11-L20","kind":"class","name":"CONSTANTS","path":"RepoCoder/utils.py","language":"python","start_line":11,"end_line":20,"context_start_line":1,"context_end_line":40,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport os\nimport glob\nimport pickle\nimport json\nimport tiktoken\nfrom transformers import AutoTokenizer\n\nclass CONSTANTS:\n # regular version for Codex\n api_benchmark = 'random_api'\n line_benchmark = 'random_line'\n # short version for CodeGen\n short_api_benchmark = 'short_api'\n short_line_benchmark = 'short_line'\n gt = 'gt'\n rg = 'r-g' # vanilla retrieval-augmented approach\n rgrg = 'r-g-r-g' # RepoCoder, two-stage retrieval and generation\n\nclass FilePathBuilder:\n api_completion_benchmark = 'datasets/random-api-completion.test.jsonl'\n random_line_completion_benchmark = 'datasets/random-line-completion.test.jsonl'\n # short version for codegen\n short_api_completion_benchmark = 'datasets/random-api-completion-short-version.test.jsonl'\n short_random_line_completion_benchmark = 'datasets/random-line-completion-short-version.test.jsonl'\n repo_base_dir = 'repositories/line_and_api_level'\n\n @staticmethod\n def make_needed_dir(file_path):\n dir_path = os.path.dirname(file_path)\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)\n\n @staticmethod\n def repo_windows_path(repo, window_size, slice_size):\n out_path = os.path.join('cache/window/repos', f'{repo}_ws{window_size}_slice{slice_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.FilePathBuilder","uri":"program://CodeT/class/RepoCoder.utils.FilePathBuilder#L22-L82","kind":"class","name":"FilePathBuilder","path":"RepoCoder/utils.py","language":"python","start_line":22,"end_line":82,"context_start_line":2,"context_end_line":102,"code":"# Licensed under the MIT license.\n\nimport os\nimport glob\nimport pickle\nimport json\nimport tiktoken\nfrom transformers import AutoTokenizer\n\nclass CONSTANTS:\n # regular version for Codex\n api_benchmark = 'random_api'\n line_benchmark = 'random_line'\n # short version for CodeGen\n short_api_benchmark = 'short_api'\n short_line_benchmark = 'short_line'\n gt = 'gt'\n rg = 'r-g' # vanilla retrieval-augmented approach\n rgrg = 'r-g-r-g' # RepoCoder, two-stage retrieval and generation\n\nclass FilePathBuilder:\n api_completion_benchmark = 'datasets/random-api-completion.test.jsonl'\n random_line_completion_benchmark = 'datasets/random-line-completion.test.jsonl'\n # short version for codegen\n short_api_completion_benchmark = 'datasets/random-api-completion-short-version.test.jsonl'\n short_random_line_completion_benchmark = 'datasets/random-line-completion-short-version.test.jsonl'\n repo_base_dir = 'repositories/line_and_api_level'\n\n @staticmethod\n def make_needed_dir(file_path):\n dir_path = os.path.dirname(file_path)\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)\n\n @staticmethod\n def repo_windows_path(repo, window_size, slice_size):\n out_path = os.path.join('cache/window/repos', f'{repo}_ws{window_size}_slice{slice_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n \n @staticmethod\n def search_first_window_path(benchmark, mode, repo, window_size):\n # mode includes gt and s-g\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def gen_first_window_path(benchmark, mode, prediction_path, repo, window_size):\n prediction_file_name = os.path.basename(prediction_path).replace('.0.jsonl', '')\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{prediction_file_name}.{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def one_gram_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.one-gram.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def ada002_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.ada002.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def retrieval_results_path(query_vector_file, repo_vector_file, max_top_k):\n retrieval_base_dir = os.path.dirname(query_vector_file.replace('/vector/', '/retrieval/'))\n query_file_name = os.path.basename(query_vector_file)\n if query_file_name.endswith('.one-gram.pkl'):\n query_file_name = query_file_name[:-len('.one-gram.pkl')]\n elif query_file_name.endswith('.ada002.pkl'):\n query_file_name = query_file_name[:-len('.ada002.pkl')]\n repo_file_name = os.path.basename(repo_vector_file)[:-len('.pkl')]\n out_path = os.path.join(retrieval_base_dir, f'{query_file_name}.{repo_file_name}.top{max_top_k}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n\nclass CodexTokenizer:\n def __init__(self):\n self.tokenizer = tiktoken.get_encoding(\"p50k_base\")\n \n def tokenize(self, text):\n # return self.tokenizer.encode(text)\n return self.tokenizer.encode_ordinary(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass CodeGenTokenizer:\n def __init__(self):\n self.tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-6B-mono')\n\n def tokenize(self, text):\n return self.tokenizer.encode(text)\n","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.CodexTokenizer","uri":"program://CodeT/class/RepoCoder.utils.CodexTokenizer#L85-L94","kind":"class","name":"CodexTokenizer","path":"RepoCoder/utils.py","language":"python","start_line":85,"end_line":94,"context_start_line":65,"context_end_line":114,"code":" def ada002_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.ada002.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def retrieval_results_path(query_vector_file, repo_vector_file, max_top_k):\n retrieval_base_dir = os.path.dirname(query_vector_file.replace('/vector/', '/retrieval/'))\n query_file_name = os.path.basename(query_vector_file)\n if query_file_name.endswith('.one-gram.pkl'):\n query_file_name = query_file_name[:-len('.one-gram.pkl')]\n elif query_file_name.endswith('.ada002.pkl'):\n query_file_name = query_file_name[:-len('.ada002.pkl')]\n repo_file_name = os.path.basename(repo_vector_file)[:-len('.pkl')]\n out_path = os.path.join(retrieval_base_dir, f'{query_file_name}.{repo_file_name}.top{max_top_k}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n\nclass CodexTokenizer:\n def __init__(self):\n self.tokenizer = tiktoken.get_encoding(\"p50k_base\")\n \n def tokenize(self, text):\n # return self.tokenizer.encode(text)\n return self.tokenizer.encode_ordinary(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass CodeGenTokenizer:\n def __init__(self):\n self.tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-6B-mono')\n\n def tokenize(self, text):\n return self.tokenizer.encode(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass Tools:\n @staticmethod\n def read_code(fname):\n with open(fname, 'r', encoding='utf8') as f:\n return f.read()\n \n @staticmethod\n def load_pickle(fname):\n with open(fname, 'rb') as f:","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.CodeGenTokenizer","uri":"program://CodeT/class/RepoCoder.utils.CodeGenTokenizer#L96-L104","kind":"class","name":"CodeGenTokenizer","path":"RepoCoder/utils.py","language":"python","start_line":96,"end_line":104,"context_start_line":76,"context_end_line":124,"code":" query_file_name = query_file_name[:-len('.one-gram.pkl')]\n elif query_file_name.endswith('.ada002.pkl'):\n query_file_name = query_file_name[:-len('.ada002.pkl')]\n repo_file_name = os.path.basename(repo_vector_file)[:-len('.pkl')]\n out_path = os.path.join(retrieval_base_dir, f'{query_file_name}.{repo_file_name}.top{max_top_k}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n\nclass CodexTokenizer:\n def __init__(self):\n self.tokenizer = tiktoken.get_encoding(\"p50k_base\")\n \n def tokenize(self, text):\n # return self.tokenizer.encode(text)\n return self.tokenizer.encode_ordinary(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass CodeGenTokenizer:\n def __init__(self):\n self.tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-6B-mono')\n\n def tokenize(self, text):\n return self.tokenizer.encode(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass Tools:\n @staticmethod\n def read_code(fname):\n with open(fname, 'r', encoding='utf8') as f:\n return f.read()\n \n @staticmethod\n def load_pickle(fname):\n with open(fname, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def dump_pickle(obj, fname):\n with open(fname, 'wb') as f:\n pickle.dump(obj, f)\n \n @staticmethod\n def dump_json(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.Tools","uri":"program://CodeT/class/RepoCoder.utils.Tools#L106-L168","kind":"class","name":"Tools","path":"RepoCoder/utils.py","language":"python","start_line":106,"end_line":168,"context_start_line":86,"context_end_line":168,"code":" def __init__(self):\n self.tokenizer = tiktoken.get_encoding(\"p50k_base\")\n \n def tokenize(self, text):\n # return self.tokenizer.encode(text)\n return self.tokenizer.encode_ordinary(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass CodeGenTokenizer:\n def __init__(self):\n self.tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-6B-mono')\n\n def tokenize(self, text):\n return self.tokenizer.encode(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass Tools:\n @staticmethod\n def read_code(fname):\n with open(fname, 'r', encoding='utf8') as f:\n return f.read()\n \n @staticmethod\n def load_pickle(fname):\n with open(fname, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def dump_pickle(obj, fname):\n with open(fname, 'wb') as f:\n pickle.dump(obj, f)\n \n @staticmethod\n def dump_json(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n json.dump(obj, f)\n\n @staticmethod\n def dump_jsonl(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n for item in obj:\n f.write(json.dumps(item) + '\\n')\n \n @staticmethod\n def load_jsonl(fname):\n with open(fname, 'r', encoding='utf8') as f:\n lines = []\n for line in f:\n lines.append(json.loads(line))\n return lines\n \n @staticmethod\n def iterate_repository(repo):\n base_dir = FilePathBuilder.repo_base_dir\n pattern = os.path.join(f'{base_dir}/{repo}', \"**\", \"*.py\")\n files = glob.glob(pattern, recursive=True)\n\n skipped_files = []\n loaded_code_files = dict()\n base_dir_list = os.path.normpath(base_dir).split(os.sep)\n for fname in files:\n try:\n code = Tools.read_code(fname)\n fpath_tuple = tuple(os.path.normpath(fname).split(os.sep)[len(base_dir_list):])\n loaded_code_files[fpath_tuple]= code\n except Exception as e:\n skipped_files.append((fname, e))\n continue\n\n if len(skipped_files) > 0:\n print(f\"Skipped {len(skipped_files)} out of {len(files)} files due to I/O errors\")\n for fname, e in skipped_files:\n print(f\"{fname}: {e}\")\n return loaded_code_files\n\n @staticmethod\n def tokenize(code):\n tokenizer = CodexTokenizer()\n return tokenizer.tokenize(code)","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.make_needed_dir","uri":"program://CodeT/function/RepoCoder.utils.make_needed_dir#L31-L34","kind":"function","name":"make_needed_dir","path":"RepoCoder/utils.py","language":"python","start_line":31,"end_line":34,"context_start_line":11,"context_end_line":54,"code":"class CONSTANTS:\n # regular version for Codex\n api_benchmark = 'random_api'\n line_benchmark = 'random_line'\n # short version for CodeGen\n short_api_benchmark = 'short_api'\n short_line_benchmark = 'short_line'\n gt = 'gt'\n rg = 'r-g' # vanilla retrieval-augmented approach\n rgrg = 'r-g-r-g' # RepoCoder, two-stage retrieval and generation\n\nclass FilePathBuilder:\n api_completion_benchmark = 'datasets/random-api-completion.test.jsonl'\n random_line_completion_benchmark = 'datasets/random-line-completion.test.jsonl'\n # short version for codegen\n short_api_completion_benchmark = 'datasets/random-api-completion-short-version.test.jsonl'\n short_random_line_completion_benchmark = 'datasets/random-line-completion-short-version.test.jsonl'\n repo_base_dir = 'repositories/line_and_api_level'\n\n @staticmethod\n def make_needed_dir(file_path):\n dir_path = os.path.dirname(file_path)\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)\n\n @staticmethod\n def repo_windows_path(repo, window_size, slice_size):\n out_path = os.path.join('cache/window/repos', f'{repo}_ws{window_size}_slice{slice_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n \n @staticmethod\n def search_first_window_path(benchmark, mode, repo, window_size):\n # mode includes gt and s-g\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def gen_first_window_path(benchmark, mode, prediction_path, repo, window_size):\n prediction_file_name = os.path.basename(prediction_path).replace('.0.jsonl', '')\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{prediction_file_name}.{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.repo_windows_path","uri":"program://CodeT/function/RepoCoder.utils.repo_windows_path#L37-L40","kind":"function","name":"repo_windows_path","path":"RepoCoder/utils.py","language":"python","start_line":37,"end_line":40,"context_start_line":17,"context_end_line":60,"code":" short_line_benchmark = 'short_line'\n gt = 'gt'\n rg = 'r-g' # vanilla retrieval-augmented approach\n rgrg = 'r-g-r-g' # RepoCoder, two-stage retrieval and generation\n\nclass FilePathBuilder:\n api_completion_benchmark = 'datasets/random-api-completion.test.jsonl'\n random_line_completion_benchmark = 'datasets/random-line-completion.test.jsonl'\n # short version for codegen\n short_api_completion_benchmark = 'datasets/random-api-completion-short-version.test.jsonl'\n short_random_line_completion_benchmark = 'datasets/random-line-completion-short-version.test.jsonl'\n repo_base_dir = 'repositories/line_and_api_level'\n\n @staticmethod\n def make_needed_dir(file_path):\n dir_path = os.path.dirname(file_path)\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)\n\n @staticmethod\n def repo_windows_path(repo, window_size, slice_size):\n out_path = os.path.join('cache/window/repos', f'{repo}_ws{window_size}_slice{slice_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n \n @staticmethod\n def search_first_window_path(benchmark, mode, repo, window_size):\n # mode includes gt and s-g\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def gen_first_window_path(benchmark, mode, prediction_path, repo, window_size):\n prediction_file_name = os.path.basename(prediction_path).replace('.0.jsonl', '')\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{prediction_file_name}.{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def one_gram_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.one-gram.pkl')","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.search_first_window_path","uri":"program://CodeT/function/RepoCoder.utils.search_first_window_path#L44-L48","kind":"function","name":"search_first_window_path","path":"RepoCoder/utils.py","language":"python","start_line":44,"end_line":48,"context_start_line":24,"context_end_line":68,"code":" random_line_completion_benchmark = 'datasets/random-line-completion.test.jsonl'\n # short version for codegen\n short_api_completion_benchmark = 'datasets/random-api-completion-short-version.test.jsonl'\n short_random_line_completion_benchmark = 'datasets/random-line-completion-short-version.test.jsonl'\n repo_base_dir = 'repositories/line_and_api_level'\n\n @staticmethod\n def make_needed_dir(file_path):\n dir_path = os.path.dirname(file_path)\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)\n\n @staticmethod\n def repo_windows_path(repo, window_size, slice_size):\n out_path = os.path.join('cache/window/repos', f'{repo}_ws{window_size}_slice{slice_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n \n @staticmethod\n def search_first_window_path(benchmark, mode, repo, window_size):\n # mode includes gt and s-g\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def gen_first_window_path(benchmark, mode, prediction_path, repo, window_size):\n prediction_file_name = os.path.basename(prediction_path).replace('.0.jsonl', '')\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{prediction_file_name}.{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def one_gram_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.one-gram.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def ada002_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.ada002.pkl')\n FilePathBuilder.make_needed_dir(out_path)","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.gen_first_window_path","uri":"program://CodeT/function/RepoCoder.utils.gen_first_window_path#L51-L55","kind":"function","name":"gen_first_window_path","path":"RepoCoder/utils.py","language":"python","start_line":51,"end_line":55,"context_start_line":31,"context_end_line":75,"code":" def make_needed_dir(file_path):\n dir_path = os.path.dirname(file_path)\n if not os.path.exists(dir_path):\n os.makedirs(dir_path)\n\n @staticmethod\n def repo_windows_path(repo, window_size, slice_size):\n out_path = os.path.join('cache/window/repos', f'{repo}_ws{window_size}_slice{slice_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n \n @staticmethod\n def search_first_window_path(benchmark, mode, repo, window_size):\n # mode includes gt and s-g\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def gen_first_window_path(benchmark, mode, prediction_path, repo, window_size):\n prediction_file_name = os.path.basename(prediction_path).replace('.0.jsonl', '')\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{prediction_file_name}.{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def one_gram_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.one-gram.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def ada002_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.ada002.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def retrieval_results_path(query_vector_file, repo_vector_file, max_top_k):\n retrieval_base_dir = os.path.dirname(query_vector_file.replace('/vector/', '/retrieval/'))\n query_file_name = os.path.basename(query_vector_file)\n if query_file_name.endswith('.one-gram.pkl'):","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.one_gram_vector_path","uri":"program://CodeT/function/RepoCoder.utils.one_gram_vector_path#L58-L62","kind":"function","name":"one_gram_vector_path","path":"RepoCoder/utils.py","language":"python","start_line":58,"end_line":62,"context_start_line":38,"context_end_line":82,"code":" out_path = os.path.join('cache/window/repos', f'{repo}_ws{window_size}_slice{slice_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n \n @staticmethod\n def search_first_window_path(benchmark, mode, repo, window_size):\n # mode includes gt and s-g\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def gen_first_window_path(benchmark, mode, prediction_path, repo, window_size):\n prediction_file_name = os.path.basename(prediction_path).replace('.0.jsonl', '')\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{prediction_file_name}.{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def one_gram_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.one-gram.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def ada002_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.ada002.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def retrieval_results_path(query_vector_file, repo_vector_file, max_top_k):\n retrieval_base_dir = os.path.dirname(query_vector_file.replace('/vector/', '/retrieval/'))\n query_file_name = os.path.basename(query_vector_file)\n if query_file_name.endswith('.one-gram.pkl'):\n query_file_name = query_file_name[:-len('.one-gram.pkl')]\n elif query_file_name.endswith('.ada002.pkl'):\n query_file_name = query_file_name[:-len('.ada002.pkl')]\n repo_file_name = os.path.basename(repo_vector_file)[:-len('.pkl')]\n out_path = os.path.join(retrieval_base_dir, f'{query_file_name}.{repo_file_name}.top{max_top_k}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.ada002_vector_path","uri":"program://CodeT/function/RepoCoder.utils.ada002_vector_path#L65-L69","kind":"function","name":"ada002_vector_path","path":"RepoCoder/utils.py","language":"python","start_line":65,"end_line":69,"context_start_line":45,"context_end_line":89,"code":" # mode includes gt and s-g\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def gen_first_window_path(benchmark, mode, prediction_path, repo, window_size):\n prediction_file_name = os.path.basename(prediction_path).replace('.0.jsonl', '')\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{prediction_file_name}.{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def one_gram_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.one-gram.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def ada002_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.ada002.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def retrieval_results_path(query_vector_file, repo_vector_file, max_top_k):\n retrieval_base_dir = os.path.dirname(query_vector_file.replace('/vector/', '/retrieval/'))\n query_file_name = os.path.basename(query_vector_file)\n if query_file_name.endswith('.one-gram.pkl'):\n query_file_name = query_file_name[:-len('.one-gram.pkl')]\n elif query_file_name.endswith('.ada002.pkl'):\n query_file_name = query_file_name[:-len('.ada002.pkl')]\n repo_file_name = os.path.basename(repo_vector_file)[:-len('.pkl')]\n out_path = os.path.join(retrieval_base_dir, f'{query_file_name}.{repo_file_name}.top{max_top_k}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n\nclass CodexTokenizer:\n def __init__(self):\n self.tokenizer = tiktoken.get_encoding(\"p50k_base\")\n \n def tokenize(self, text):","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.retrieval_results_path","uri":"program://CodeT/function/RepoCoder.utils.retrieval_results_path#L72-L82","kind":"function","name":"retrieval_results_path","path":"RepoCoder/utils.py","language":"python","start_line":72,"end_line":82,"context_start_line":52,"context_end_line":102,"code":" prediction_file_name = os.path.basename(prediction_path).replace('.0.jsonl', '')\n out_path = os.path.join(f'cache/window/{benchmark}/{mode}', f'{prediction_file_name}.{repo}_ws{window_size}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def one_gram_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.one-gram.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def ada002_vector_path(window_file):\n vector_path = window_file.replace('/window/', '/vector/')\n out_path = vector_path.replace('.pkl', '.ada002.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n @staticmethod\n def retrieval_results_path(query_vector_file, repo_vector_file, max_top_k):\n retrieval_base_dir = os.path.dirname(query_vector_file.replace('/vector/', '/retrieval/'))\n query_file_name = os.path.basename(query_vector_file)\n if query_file_name.endswith('.one-gram.pkl'):\n query_file_name = query_file_name[:-len('.one-gram.pkl')]\n elif query_file_name.endswith('.ada002.pkl'):\n query_file_name = query_file_name[:-len('.ada002.pkl')]\n repo_file_name = os.path.basename(repo_vector_file)[:-len('.pkl')]\n out_path = os.path.join(retrieval_base_dir, f'{query_file_name}.{repo_file_name}.top{max_top_k}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n\nclass CodexTokenizer:\n def __init__(self):\n self.tokenizer = tiktoken.get_encoding(\"p50k_base\")\n \n def tokenize(self, text):\n # return self.tokenizer.encode(text)\n return self.tokenizer.encode_ordinary(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass CodeGenTokenizer:\n def __init__(self):\n self.tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-6B-mono')\n\n def tokenize(self, text):\n return self.tokenizer.encode(text)\n","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.__init__","uri":"program://CodeT/function/RepoCoder.utils.__init__#L97-L98","kind":"function","name":"__init__","path":"RepoCoder/utils.py","language":"python","start_line":97,"end_line":98,"context_start_line":77,"context_end_line":118,"code":" elif query_file_name.endswith('.ada002.pkl'):\n query_file_name = query_file_name[:-len('.ada002.pkl')]\n repo_file_name = os.path.basename(repo_vector_file)[:-len('.pkl')]\n out_path = os.path.join(retrieval_base_dir, f'{query_file_name}.{repo_file_name}.top{max_top_k}.pkl')\n FilePathBuilder.make_needed_dir(out_path)\n return out_path\n\n\nclass CodexTokenizer:\n def __init__(self):\n self.tokenizer = tiktoken.get_encoding(\"p50k_base\")\n \n def tokenize(self, text):\n # return self.tokenizer.encode(text)\n return self.tokenizer.encode_ordinary(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass CodeGenTokenizer:\n def __init__(self):\n self.tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-6B-mono')\n\n def tokenize(self, text):\n return self.tokenizer.encode(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass Tools:\n @staticmethod\n def read_code(fname):\n with open(fname, 'r', encoding='utf8') as f:\n return f.read()\n \n @staticmethod\n def load_pickle(fname):\n with open(fname, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def dump_pickle(obj, fname):","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.tokenize","uri":"program://CodeT/function/RepoCoder.utils.tokenize#L166-L168","kind":"function","name":"tokenize","path":"RepoCoder/utils.py","language":"python","start_line":166,"end_line":168,"context_start_line":146,"context_end_line":168,"code":"\n skipped_files = []\n loaded_code_files = dict()\n base_dir_list = os.path.normpath(base_dir).split(os.sep)\n for fname in files:\n try:\n code = Tools.read_code(fname)\n fpath_tuple = tuple(os.path.normpath(fname).split(os.sep)[len(base_dir_list):])\n loaded_code_files[fpath_tuple]= code\n except Exception as e:\n skipped_files.append((fname, e))\n continue\n\n if len(skipped_files) > 0:\n print(f\"Skipped {len(skipped_files)} out of {len(files)} files due to I/O errors\")\n for fname, e in skipped_files:\n print(f\"{fname}: {e}\")\n return loaded_code_files\n\n @staticmethod\n def tokenize(code):\n tokenizer = CodexTokenizer()\n return tokenizer.tokenize(code)","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.decode","uri":"program://CodeT/function/RepoCoder.utils.decode#L103-L104","kind":"function","name":"decode","path":"RepoCoder/utils.py","language":"python","start_line":103,"end_line":104,"context_start_line":83,"context_end_line":124,"code":"\n\nclass CodexTokenizer:\n def __init__(self):\n self.tokenizer = tiktoken.get_encoding(\"p50k_base\")\n \n def tokenize(self, text):\n # return self.tokenizer.encode(text)\n return self.tokenizer.encode_ordinary(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass CodeGenTokenizer:\n def __init__(self):\n self.tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-6B-mono')\n\n def tokenize(self, text):\n return self.tokenizer.encode(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass Tools:\n @staticmethod\n def read_code(fname):\n with open(fname, 'r', encoding='utf8') as f:\n return f.read()\n \n @staticmethod\n def load_pickle(fname):\n with open(fname, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def dump_pickle(obj, fname):\n with open(fname, 'wb') as f:\n pickle.dump(obj, f)\n \n @staticmethod\n def dump_json(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.read_code","uri":"program://CodeT/function/RepoCoder.utils.read_code#L108-L110","kind":"function","name":"read_code","path":"RepoCoder/utils.py","language":"python","start_line":108,"end_line":110,"context_start_line":88,"context_end_line":130,"code":" \n def tokenize(self, text):\n # return self.tokenizer.encode(text)\n return self.tokenizer.encode_ordinary(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass CodeGenTokenizer:\n def __init__(self):\n self.tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-6B-mono')\n\n def tokenize(self, text):\n return self.tokenizer.encode(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass Tools:\n @staticmethod\n def read_code(fname):\n with open(fname, 'r', encoding='utf8') as f:\n return f.read()\n \n @staticmethod\n def load_pickle(fname):\n with open(fname, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def dump_pickle(obj, fname):\n with open(fname, 'wb') as f:\n pickle.dump(obj, f)\n \n @staticmethod\n def dump_json(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n json.dump(obj, f)\n\n @staticmethod\n def dump_jsonl(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n for item in obj:","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.load_pickle","uri":"program://CodeT/function/RepoCoder.utils.load_pickle#L113-L115","kind":"function","name":"load_pickle","path":"RepoCoder/utils.py","language":"python","start_line":113,"end_line":115,"context_start_line":93,"context_end_line":135,"code":" def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass CodeGenTokenizer:\n def __init__(self):\n self.tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-6B-mono')\n\n def tokenize(self, text):\n return self.tokenizer.encode(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass Tools:\n @staticmethod\n def read_code(fname):\n with open(fname, 'r', encoding='utf8') as f:\n return f.read()\n \n @staticmethod\n def load_pickle(fname):\n with open(fname, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def dump_pickle(obj, fname):\n with open(fname, 'wb') as f:\n pickle.dump(obj, f)\n \n @staticmethod\n def dump_json(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n json.dump(obj, f)\n\n @staticmethod\n def dump_jsonl(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n for item in obj:\n f.write(json.dumps(item) + '\\n')\n \n @staticmethod\n def load_jsonl(fname):\n with open(fname, 'r', encoding='utf8') as f:","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.dump_pickle","uri":"program://CodeT/function/RepoCoder.utils.dump_pickle#L118-L120","kind":"function","name":"dump_pickle","path":"RepoCoder/utils.py","language":"python","start_line":118,"end_line":120,"context_start_line":98,"context_end_line":140,"code":" self.tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-6B-mono')\n\n def tokenize(self, text):\n return self.tokenizer.encode(text)\n\n def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass Tools:\n @staticmethod\n def read_code(fname):\n with open(fname, 'r', encoding='utf8') as f:\n return f.read()\n \n @staticmethod\n def load_pickle(fname):\n with open(fname, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def dump_pickle(obj, fname):\n with open(fname, 'wb') as f:\n pickle.dump(obj, f)\n \n @staticmethod\n def dump_json(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n json.dump(obj, f)\n\n @staticmethod\n def dump_jsonl(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n for item in obj:\n f.write(json.dumps(item) + '\\n')\n \n @staticmethod\n def load_jsonl(fname):\n with open(fname, 'r', encoding='utf8') as f:\n lines = []\n for line in f:\n lines.append(json.loads(line))\n return lines\n ","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.dump_json","uri":"program://CodeT/function/RepoCoder.utils.dump_json#L123-L125","kind":"function","name":"dump_json","path":"RepoCoder/utils.py","language":"python","start_line":123,"end_line":125,"context_start_line":103,"context_end_line":145,"code":" def decode(self, token_ids):\n return self.tokenizer.decode(token_ids)\n\nclass Tools:\n @staticmethod\n def read_code(fname):\n with open(fname, 'r', encoding='utf8') as f:\n return f.read()\n \n @staticmethod\n def load_pickle(fname):\n with open(fname, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def dump_pickle(obj, fname):\n with open(fname, 'wb') as f:\n pickle.dump(obj, f)\n \n @staticmethod\n def dump_json(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n json.dump(obj, f)\n\n @staticmethod\n def dump_jsonl(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n for item in obj:\n f.write(json.dumps(item) + '\\n')\n \n @staticmethod\n def load_jsonl(fname):\n with open(fname, 'r', encoding='utf8') as f:\n lines = []\n for line in f:\n lines.append(json.loads(line))\n return lines\n \n @staticmethod\n def iterate_repository(repo):\n base_dir = FilePathBuilder.repo_base_dir\n pattern = os.path.join(f'{base_dir}/{repo}', \"**\", \"*.py\")\n files = glob.glob(pattern, recursive=True)","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.dump_jsonl","uri":"program://CodeT/function/RepoCoder.utils.dump_jsonl#L128-L131","kind":"function","name":"dump_jsonl","path":"RepoCoder/utils.py","language":"python","start_line":128,"end_line":131,"context_start_line":108,"context_end_line":151,"code":" def read_code(fname):\n with open(fname, 'r', encoding='utf8') as f:\n return f.read()\n \n @staticmethod\n def load_pickle(fname):\n with open(fname, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def dump_pickle(obj, fname):\n with open(fname, 'wb') as f:\n pickle.dump(obj, f)\n \n @staticmethod\n def dump_json(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n json.dump(obj, f)\n\n @staticmethod\n def dump_jsonl(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n for item in obj:\n f.write(json.dumps(item) + '\\n')\n \n @staticmethod\n def load_jsonl(fname):\n with open(fname, 'r', encoding='utf8') as f:\n lines = []\n for line in f:\n lines.append(json.loads(line))\n return lines\n \n @staticmethod\n def iterate_repository(repo):\n base_dir = FilePathBuilder.repo_base_dir\n pattern = os.path.join(f'{base_dir}/{repo}', \"**\", \"*.py\")\n files = glob.glob(pattern, recursive=True)\n\n skipped_files = []\n loaded_code_files = dict()\n base_dir_list = os.path.normpath(base_dir).split(os.sep)\n for fname in files:\n try:","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.load_jsonl","uri":"program://CodeT/function/RepoCoder.utils.load_jsonl#L134-L139","kind":"function","name":"load_jsonl","path":"RepoCoder/utils.py","language":"python","start_line":134,"end_line":139,"context_start_line":114,"context_end_line":159,"code":" with open(fname, 'rb') as f:\n return pickle.load(f)\n \n @staticmethod\n def dump_pickle(obj, fname):\n with open(fname, 'wb') as f:\n pickle.dump(obj, f)\n \n @staticmethod\n def dump_json(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n json.dump(obj, f)\n\n @staticmethod\n def dump_jsonl(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n for item in obj:\n f.write(json.dumps(item) + '\\n')\n \n @staticmethod\n def load_jsonl(fname):\n with open(fname, 'r', encoding='utf8') as f:\n lines = []\n for line in f:\n lines.append(json.loads(line))\n return lines\n \n @staticmethod\n def iterate_repository(repo):\n base_dir = FilePathBuilder.repo_base_dir\n pattern = os.path.join(f'{base_dir}/{repo}', \"**\", \"*.py\")\n files = glob.glob(pattern, recursive=True)\n\n skipped_files = []\n loaded_code_files = dict()\n base_dir_list = os.path.normpath(base_dir).split(os.sep)\n for fname in files:\n try:\n code = Tools.read_code(fname)\n fpath_tuple = tuple(os.path.normpath(fname).split(os.sep)[len(base_dir_list):])\n loaded_code_files[fpath_tuple]= code\n except Exception as e:\n skipped_files.append((fname, e))\n continue\n\n if len(skipped_files) > 0:","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.utils.iterate_repository","uri":"program://CodeT/function/RepoCoder.utils.iterate_repository#L142-L163","kind":"function","name":"iterate_repository","path":"RepoCoder/utils.py","language":"python","start_line":142,"end_line":163,"context_start_line":122,"context_end_line":168,"code":" @staticmethod\n def dump_json(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n json.dump(obj, f)\n\n @staticmethod\n def dump_jsonl(obj, fname):\n with open(fname, 'w', encoding='utf8') as f:\n for item in obj:\n f.write(json.dumps(item) + '\\n')\n \n @staticmethod\n def load_jsonl(fname):\n with open(fname, 'r', encoding='utf8') as f:\n lines = []\n for line in f:\n lines.append(json.loads(line))\n return lines\n \n @staticmethod\n def iterate_repository(repo):\n base_dir = FilePathBuilder.repo_base_dir\n pattern = os.path.join(f'{base_dir}/{repo}', \"**\", \"*.py\")\n files = glob.glob(pattern, recursive=True)\n\n skipped_files = []\n loaded_code_files = dict()\n base_dir_list = os.path.normpath(base_dir).split(os.sep)\n for fname in files:\n try:\n code = Tools.read_code(fname)\n fpath_tuple = tuple(os.path.normpath(fname).split(os.sep)[len(base_dir_list):])\n loaded_code_files[fpath_tuple]= code\n except Exception as e:\n skipped_files.append((fname, e))\n continue\n\n if len(skipped_files) > 0:\n print(f\"Skipped {len(skipped_files)} out of {len(files)} files due to I/O errors\")\n for fname, e in skipped_files:\n print(f\"{fname}: {e}\")\n return loaded_code_files\n\n @staticmethod\n def tokenize(code):\n tokenizer = CodexTokenizer()\n return tokenizer.tokenize(code)","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.compute_score","uri":"program://CodeT/module/RepoCoder.compute_score#L1-L67","kind":"module","name":"RepoCoder.compute_score","path":"RepoCoder/compute_score.py","language":"python","start_line":1,"end_line":67,"context_start_line":1,"context_end_line":67,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport editdistance\nfrom collections import defaultdict\n\nfrom utils import Tools\n\ndef compute_EM(target, predictions, passk):\n target_lines = [line.strip() for line in target.splitlines() if line.strip()]\n EM_scores = []\n for prediction in predictions[:passk]:\n prediction_lines = [line.strip() for line in prediction.splitlines() if line.strip()][:len(target_lines)]\n if len(target_lines) != len(prediction_lines):\n EM_scores.append(0)\n continue\n if target_lines == prediction_lines:\n EM_scores.append(1)\n continue\n EM_scores.append(0)\n return any(EM_scores)\n\ndef compute_ES(target, predictions, passk):\n target_lines = [line.strip() for line in target.splitlines() if line.strip()]\n target_str = '\\n'.join(target_lines)\n ES_scores = []\n for prediction in predictions[:passk]:\n prediction_lines = [line.strip() for line in prediction.splitlines() if line.strip()][:len(target_lines)]\n prediction_str = '\\n'.join(prediction_lines)\n ES_scores.append(\n 1 - (editdistance.eval(target_str, prediction_str) / max(len(target_str), len(prediction_str)))\n )\n return max(ES_scores)\n\ndef compute_score_by_repo_with_metadata(repos, lines, stype, passk=1):\n scores = defaultdict(list)\n for line in lines:\n repo = line['metadata']['task_id'].split('/')[0]\n if repo not in repos:\n continue\n samples = [line['choices'][i]['text'] for i in range(len(line['choices']))]\n if stype == 'EM':\n score = compute_EM(line['metadata']['ground_truth'], samples, passk)\n elif stype == 'ES':\n score = compute_ES(line['metadata']['ground_truth'], samples, passk)\n scores[repo].append(score)\n avg_scores = {repo: round(sum(scores[repo]) / len(scores[repo]), 4) for repo in scores}\n repo_count = {repo: len(scores[repo]) for repo in scores}\n print(stype)\n for repo in avg_scores.keys():\n print(f'{avg_scores[repo]}\\t{repo_count[repo]}\\t{repo}')\n\nif __name__ == '__main__':\n repos = [\n 'huggingface_diffusers',\n 'nerfstudio-project_nerfstudio',\n 'awslabs_fortuna',\n 'huggingface_evaluate',\n 'google_vizier',\n 'alibaba_FederatedScope',\n 'pytorch_rl',\n 'opendilab_ACE',\n ]\n '''compute single prediction'''\n file_path = 'output/line-rgrg-ada-ws-20-ss-2_samples.0.jsonl'\n compute_score_by_repo_with_metadata(repos, Tools.load_jsonl(file_path), 'EM', passk=1)\n compute_score_by_repo_with_metadata(repos, Tools.load_jsonl(file_path), 'ES', passk=1)","source_hash":"3a572b3b8f537d4e269ba10cd0accf62a3f517f754a1c1dd2a84630b880cca4c","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.compute_score.compute_EM","uri":"program://CodeT/function/RepoCoder.compute_score.compute_EM#L9-L21","kind":"function","name":"compute_EM","path":"RepoCoder/compute_score.py","language":"python","start_line":9,"end_line":21,"context_start_line":1,"context_end_line":41,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport editdistance\nfrom collections import defaultdict\n\nfrom utils import Tools\n\ndef compute_EM(target, predictions, passk):\n target_lines = [line.strip() for line in target.splitlines() if line.strip()]\n EM_scores = []\n for prediction in predictions[:passk]:\n prediction_lines = [line.strip() for line in prediction.splitlines() if line.strip()][:len(target_lines)]\n if len(target_lines) != len(prediction_lines):\n EM_scores.append(0)\n continue\n if target_lines == prediction_lines:\n EM_scores.append(1)\n continue\n EM_scores.append(0)\n return any(EM_scores)\n\ndef compute_ES(target, predictions, passk):\n target_lines = [line.strip() for line in target.splitlines() if line.strip()]\n target_str = '\\n'.join(target_lines)\n ES_scores = []\n for prediction in predictions[:passk]:\n prediction_lines = [line.strip() for line in prediction.splitlines() if line.strip()][:len(target_lines)]\n prediction_str = '\\n'.join(prediction_lines)\n ES_scores.append(\n 1 - (editdistance.eval(target_str, prediction_str) / max(len(target_str), len(prediction_str)))\n )\n return max(ES_scores)\n\ndef compute_score_by_repo_with_metadata(repos, lines, stype, passk=1):\n scores = defaultdict(list)\n for line in lines:\n repo = line['metadata']['task_id'].split('/')[0]\n if repo not in repos:\n continue\n samples = [line['choices'][i]['text'] for i in range(len(line['choices']))]","source_hash":"3a572b3b8f537d4e269ba10cd0accf62a3f517f754a1c1dd2a84630b880cca4c","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.compute_score.compute_ES","uri":"program://CodeT/function/RepoCoder.compute_score.compute_ES#L23-L33","kind":"function","name":"compute_ES","path":"RepoCoder/compute_score.py","language":"python","start_line":23,"end_line":33,"context_start_line":3,"context_end_line":53,"code":"\nimport editdistance\nfrom collections import defaultdict\n\nfrom utils import Tools\n\ndef compute_EM(target, predictions, passk):\n target_lines = [line.strip() for line in target.splitlines() if line.strip()]\n EM_scores = []\n for prediction in predictions[:passk]:\n prediction_lines = [line.strip() for line in prediction.splitlines() if line.strip()][:len(target_lines)]\n if len(target_lines) != len(prediction_lines):\n EM_scores.append(0)\n continue\n if target_lines == prediction_lines:\n EM_scores.append(1)\n continue\n EM_scores.append(0)\n return any(EM_scores)\n\ndef compute_ES(target, predictions, passk):\n target_lines = [line.strip() for line in target.splitlines() if line.strip()]\n target_str = '\\n'.join(target_lines)\n ES_scores = []\n for prediction in predictions[:passk]:\n prediction_lines = [line.strip() for line in prediction.splitlines() if line.strip()][:len(target_lines)]\n prediction_str = '\\n'.join(prediction_lines)\n ES_scores.append(\n 1 - (editdistance.eval(target_str, prediction_str) / max(len(target_str), len(prediction_str)))\n )\n return max(ES_scores)\n\ndef compute_score_by_repo_with_metadata(repos, lines, stype, passk=1):\n scores = defaultdict(list)\n for line in lines:\n repo = line['metadata']['task_id'].split('/')[0]\n if repo not in repos:\n continue\n samples = [line['choices'][i]['text'] for i in range(len(line['choices']))]\n if stype == 'EM':\n score = compute_EM(line['metadata']['ground_truth'], samples, passk)\n elif stype == 'ES':\n score = compute_ES(line['metadata']['ground_truth'], samples, passk)\n scores[repo].append(score)\n avg_scores = {repo: round(sum(scores[repo]) / len(scores[repo]), 4) for repo in scores}\n repo_count = {repo: len(scores[repo]) for repo in scores}\n print(stype)\n for repo in avg_scores.keys():\n print(f'{avg_scores[repo]}\\t{repo_count[repo]}\\t{repo}')\n\nif __name__ == '__main__':","source_hash":"3a572b3b8f537d4e269ba10cd0accf62a3f517f754a1c1dd2a84630b880cca4c","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.compute_score.compute_score_by_repo_with_metadata","uri":"program://CodeT/function/RepoCoder.compute_score.compute_score_by_repo_with_metadata#L35-L51","kind":"function","name":"compute_score_by_repo_with_metadata","path":"RepoCoder/compute_score.py","language":"python","start_line":35,"end_line":51,"context_start_line":15,"context_end_line":67,"code":" EM_scores.append(0)\n continue\n if target_lines == prediction_lines:\n EM_scores.append(1)\n continue\n EM_scores.append(0)\n return any(EM_scores)\n\ndef compute_ES(target, predictions, passk):\n target_lines = [line.strip() for line in target.splitlines() if line.strip()]\n target_str = '\\n'.join(target_lines)\n ES_scores = []\n for prediction in predictions[:passk]:\n prediction_lines = [line.strip() for line in prediction.splitlines() if line.strip()][:len(target_lines)]\n prediction_str = '\\n'.join(prediction_lines)\n ES_scores.append(\n 1 - (editdistance.eval(target_str, prediction_str) / max(len(target_str), len(prediction_str)))\n )\n return max(ES_scores)\n\ndef compute_score_by_repo_with_metadata(repos, lines, stype, passk=1):\n scores = defaultdict(list)\n for line in lines:\n repo = line['metadata']['task_id'].split('/')[0]\n if repo not in repos:\n continue\n samples = [line['choices'][i]['text'] for i in range(len(line['choices']))]\n if stype == 'EM':\n score = compute_EM(line['metadata']['ground_truth'], samples, passk)\n elif stype == 'ES':\n score = compute_ES(line['metadata']['ground_truth'], samples, passk)\n scores[repo].append(score)\n avg_scores = {repo: round(sum(scores[repo]) / len(scores[repo]), 4) for repo in scores}\n repo_count = {repo: len(scores[repo]) for repo in scores}\n print(stype)\n for repo in avg_scores.keys():\n print(f'{avg_scores[repo]}\\t{repo_count[repo]}\\t{repo}')\n\nif __name__ == '__main__':\n repos = [\n 'huggingface_diffusers',\n 'nerfstudio-project_nerfstudio',\n 'awslabs_fortuna',\n 'huggingface_evaluate',\n 'google_vizier',\n 'alibaba_FederatedScope',\n 'pytorch_rl',\n 'opendilab_ACE',\n ]\n '''compute single prediction'''\n file_path = 'output/line-rgrg-ada-ws-20-ss-2_samples.0.jsonl'\n compute_score_by_repo_with_metadata(repos, Tools.load_jsonl(file_path), 'EM', passk=1)\n compute_score_by_repo_with_metadata(repos, Tools.load_jsonl(file_path), 'ES', passk=1)","source_hash":"3a572b3b8f537d4e269ba10cd0accf62a3f517f754a1c1dd2a84630b880cca4c","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_prompt","uri":"program://CodeT/module/RepoCoder.build_prompt#L1-L162","kind":"module","name":"RepoCoder.build_prompt","path":"RepoCoder/build_prompt.py","language":"python","start_line":1,"end_line":162,"context_start_line":1,"context_end_line":162,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport functools\nimport os\n\nfrom utils import Tools, FilePathBuilder, CodexTokenizer, CodeGenTokenizer, CONSTANTS\n\nclass PromptBuilder:\n def __init__(self, query_lines_with_retrieval_results, task_path, log_message, tokenizer):\n self.query_lines_with_retrieval_results = query_lines_with_retrieval_results\n self.log_message = log_message\n if tokenizer == CodexTokenizer:\n self.tokenizer = CodexTokenizer()\n self.max_retrieval_length = 2000 # half of the max length of the model\n elif tokenizer == CodeGenTokenizer:\n self.tokenizer = CodeGenTokenizer()\n self.max_retrieval_length = 1000\n tasks = Tools.load_jsonl(task_path)\n self.tasks_by_task_id = {task['metadata']['task_id']: task for task in tasks}\n self.seperator = '# ' + '-' * 50\n self.max_examples = 10 # maximum number of examples to be included in the prompt\n\n def _make_a_block(self, retrieved_context):\n content, sim_score = retrieved_context\n metadata = content['metadata']\n # put the file path in the comment\n assert metadata[0]['fpath_tuple'][0] == metadata[0]['repo']\n f_paths = ['/'.join(x['fpath_tuple'][1:]) for x in metadata]\n f_paths_str = '\\n'.join([f'# {f_path}' for f_path in f_paths])\n f_path_comment = f'# the below code fragment can be found in:'\n # put code lines in the comment\n content_lines = content['context'].splitlines()\n content_lines_comment = [f'# {line}' for line in content_lines]\n # aggregate the comment and the code lines\n \n block_str = '\\n'.join([f_path_comment, f_paths_str, self.seperator] + content_lines_comment + [self.seperator]) + '\\n'\n tokenized_block = self.tokenizer.tokenize(block_str)\n token_len = len(tokenized_block)\n return block_str, token_len\n\n def _make_an_extended_block(self, retrieved_context):\n content, sim_score = retrieved_context\n metadata = content['metadata']\n # put the file path in the comment\n assert metadata[0]['fpath_tuple'][0] == metadata[0]['repo']\n f_paths = ['/'.join(x['fpath_tuple'][1:]) for x in metadata]\n f_paths_str = '\\n'.join([f'# {f_path}' for f_path in f_paths])\n f_path_comment = f'# the below code fragment can be found in:'\n # put code lines in the comment\n original_code = Tools.read_code(os.path.join(FilePathBuilder.repo_base_dir, *metadata[0]['fpath_tuple']))\n code_lines = original_code.splitlines()\n end_line_no = metadata[0]['end_line_no']\n window_size = metadata[0]['window_size']\n slice_size = metadata[0]['slice_size']\n new_end_line_no = min(end_line_no + window_size // slice_size, len(code_lines))\n new_start_line_no = max(0, new_end_line_no - window_size)\n content_lines = code_lines[new_start_line_no:new_end_line_no]\n content_lines_comment = [f'# {line}' for line in content_lines]\n # aggregate the comment and the code lines\n block_str = '\\n'.join([f_path_comment, f_paths_str, self.seperator] + content_lines_comment + [self.seperator]) + '\\n'\n tokenized_block = self.tokenizer.tokenize(block_str)\n token_len = len(tokenized_block)\n return block_str, token_len\n\n def _build_prompt(self, mode, prompt, top_k_context):\n prepend_context = \"# Here are some relevant code fragments from other files of the repo:\\n\"\n prepend_context += self.seperator + '\\n'\n current_token_length = 20 # the length of the head_prompt, same for codex and codegen tokenizer\n prepend_blocks = []\n chosen_context = []\n make_block_func = self._make_an_extended_block if mode == CONSTANTS.rg else self._make_a_block\n for retrieved_context in top_k_context[::-1]:\n if len(chosen_context) >= self.max_examples:\n break\n block_str, token_len = make_block_func(retrieved_context)\n if current_token_length + token_len < self.max_retrieval_length:\n prepend_blocks.insert(0, block_str) \n current_token_length += token_len\n chosen_context.append(retrieved_context)\n else:\n continue\n prepend_context += ''.join(prepend_blocks) # all the blocks already have a line break at the end\n return prepend_context + '\\n' + prompt, chosen_context\n\n def build_2nd_stage_input_file(self, mode):\n new_prompt_lines = []\n for query_line in self.query_lines_with_retrieval_results:\n task_id = query_line['metadata']['task_id']\n task = self.tasks_by_task_id[task_id]\n old_prompt = task['prompt']\n top_k_context = query_line['top_k_context']\n new_prompt, chosen_context = self._build_prompt(mode, old_prompt, top_k_context)\n new_prompt_line = {\n 'prompt': new_prompt,\n 'metadata': task['metadata'],\n }\n new_prompt_line['metadata']['query_window'] = {\n 'context': query_line['context'],\n 'metadata': query_line['metadata'],\n }\n new_prompt_line['metadata']['top_k_context'] = [\n {\n 'context': x[0]['context'],\n 'metadata': x[0]['metadata'],\n 'sim_score': x[1],\n } for x in chosen_context\n ]\n new_prompt_line['metadata']['window_size'] = query_line['metadata']['window_size']\n new_prompt_line['metadata']['slice_size'] = chosen_context[0][0]['metadata'][0]['slice_size']\n new_prompt_lines.append(new_prompt_line)\n print('done! ' + self.log_message)\n return new_prompt_lines\n\nclass BuildPromptWrapper:\n def __init__(self, vectorizer, benchmark, repos, window_size, slice_size, tokenizer):\n if vectorizer == 'one-gram':\n self.vector_path_builder = FilePathBuilder.one_gram_vector_path\n elif vectorizer == 'ada002':\n self.vector_path_builder = FilePathBuilder.ada002_vector_path\n self.max_top_k = 20\n self.repos = repos\n self.window_size = window_size\n self.slice_size = slice_size\n if benchmark == CONSTANTS.line_benchmark:\n self.task_path = FilePathBuilder.random_line_completion_benchmark\n elif benchmark == CONSTANTS.api_benchmark:\n self.task_path = FilePathBuilder.api_completion_benchmark\n elif benchmark == CONSTANTS.short_api_benchmark:\n self.task_path = FilePathBuilder.short_api_completion_benchmark\n elif benchmark == CONSTANTS.short_line_benchmark:\n self.task_path = FilePathBuilder.short_random_line_completion_benchmark\n self.benchmark = benchmark\n self.tokenizer = tokenizer\n \n def _run(self, mode, query_window_path_builder, output_file_path):\n workers = []\n for repo in self.repos:\n query_window_path = query_window_path_builder(repo, self.window_size)\n query_line_path = self.vector_path_builder(query_window_path)\n repo_window_path = FilePathBuilder.repo_windows_path(repo, self.window_size, self.slice_size)\n repo_embedding_path = self.vector_path_builder(repo_window_path)\n retrieval_results = FilePathBuilder.retrieval_results_path(query_line_path, repo_embedding_path, self.max_top_k)\n \n query_lines_with_retrieval_results = Tools.load_pickle(retrieval_results)\n log_message = f'repo: {repo}, window: {self.window_size}, slice: {self.slice_size}'\n worker = PromptBuilder(query_lines_with_retrieval_results, self.task_path, log_message, self.tokenizer)\n workers.append(worker)\n lines = []\n for worker in workers:\n lines += worker.build_2nd_stage_input_file(mode)\n Tools.dump_jsonl(lines, output_file_path)\n\n def build_first_search_prompt(self, mode, output_path):\n query_line_path_temp = functools.partial(FilePathBuilder.search_first_window_path, self.benchmark, mode)\n self._run(mode, query_line_path_temp, output_path)\n\n \n def build_prediction_prompt(self, mode, prediction_path, output_path):\n query_line_path_temp = functools.partial(FilePathBuilder.gen_first_window_path, self.benchmark, mode, prediction_path)\n self._run(mode, query_line_path_temp, output_path)\n","source_hash":"1907f0b26ba4fbd02b1b43cf88c0478b8ee9cad7e6fe15e5c769a8863c10039b","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_prompt.PromptBuilder","uri":"program://CodeT/class/RepoCoder.build_prompt.PromptBuilder#L9-L113","kind":"class","name":"PromptBuilder","path":"RepoCoder/build_prompt.py","language":"python","start_line":9,"end_line":113,"context_start_line":1,"context_end_line":133,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport functools\nimport os\n\nfrom utils import Tools, FilePathBuilder, CodexTokenizer, CodeGenTokenizer, CONSTANTS\n\nclass PromptBuilder:\n def __init__(self, query_lines_with_retrieval_results, task_path, log_message, tokenizer):\n self.query_lines_with_retrieval_results = query_lines_with_retrieval_results\n self.log_message = log_message\n if tokenizer == CodexTokenizer:\n self.tokenizer = CodexTokenizer()\n self.max_retrieval_length = 2000 # half of the max length of the model\n elif tokenizer == CodeGenTokenizer:\n self.tokenizer = CodeGenTokenizer()\n self.max_retrieval_length = 1000\n tasks = Tools.load_jsonl(task_path)\n self.tasks_by_task_id = {task['metadata']['task_id']: task for task in tasks}\n self.seperator = '# ' + '-' * 50\n self.max_examples = 10 # maximum number of examples to be included in the prompt\n\n def _make_a_block(self, retrieved_context):\n content, sim_score = retrieved_context\n metadata = content['metadata']\n # put the file path in the comment\n assert metadata[0]['fpath_tuple'][0] == metadata[0]['repo']\n f_paths = ['/'.join(x['fpath_tuple'][1:]) for x in metadata]\n f_paths_str = '\\n'.join([f'# {f_path}' for f_path in f_paths])\n f_path_comment = f'# the below code fragment can be found in:'\n # put code lines in the comment\n content_lines = content['context'].splitlines()\n content_lines_comment = [f'# {line}' for line in content_lines]\n # aggregate the comment and the code lines\n \n block_str = '\\n'.join([f_path_comment, f_paths_str, self.seperator] + content_lines_comment + [self.seperator]) + '\\n'\n tokenized_block = self.tokenizer.tokenize(block_str)\n token_len = len(tokenized_block)\n return block_str, token_len\n\n def _make_an_extended_block(self, retrieved_context):\n content, sim_score = retrieved_context\n metadata = content['metadata']\n # put the file path in the comment\n assert metadata[0]['fpath_tuple'][0] == metadata[0]['repo']\n f_paths = ['/'.join(x['fpath_tuple'][1:]) for x in metadata]\n f_paths_str = '\\n'.join([f'# {f_path}' for f_path in f_paths])\n f_path_comment = f'# the below code fragment can be found in:'\n # put code lines in the comment\n original_code = Tools.read_code(os.path.join(FilePathBuilder.repo_base_dir, *metadata[0]['fpath_tuple']))\n code_lines = original_code.splitlines()\n end_line_no = metadata[0]['end_line_no']\n window_size = metadata[0]['window_size']\n slice_size = metadata[0]['slice_size']\n new_end_line_no = min(end_line_no + window_size // slice_size, len(code_lines))\n new_start_line_no = max(0, new_end_line_no - window_size)\n content_lines = code_lines[new_start_line_no:new_end_line_no]\n content_lines_comment = [f'# {line}' for line in content_lines]\n # aggregate the comment and the code lines\n block_str = '\\n'.join([f_path_comment, f_paths_str, self.seperator] + content_lines_comment + [self.seperator]) + '\\n'\n tokenized_block = self.tokenizer.tokenize(block_str)\n token_len = len(tokenized_block)\n return block_str, token_len\n\n def _build_prompt(self, mode, prompt, top_k_context):\n prepend_context = \"# Here are some relevant code fragments from other files of the repo:\\n\"\n prepend_context += self.seperator + '\\n'\n current_token_length = 20 # the length of the head_prompt, same for codex and codegen tokenizer\n prepend_blocks = []\n chosen_context = []\n make_block_func = self._make_an_extended_block if mode == CONSTANTS.rg else self._make_a_block\n for retrieved_context in top_k_context[::-1]:\n if len(chosen_context) >= self.max_examples:\n break\n block_str, token_len = make_block_func(retrieved_context)\n if current_token_length + token_len < self.max_retrieval_length:\n prepend_blocks.insert(0, block_str) \n current_token_length += token_len\n chosen_context.append(retrieved_context)\n else:\n continue\n prepend_context += ''.join(prepend_blocks) # all the blocks already have a line break at the end\n return prepend_context + '\\n' + prompt, chosen_context\n\n def build_2nd_stage_input_file(self, mode):\n new_prompt_lines = []\n for query_line in self.query_lines_with_retrieval_results:\n task_id = query_line['metadata']['task_id']\n task = self.tasks_by_task_id[task_id]\n old_prompt = task['prompt']\n top_k_context = query_line['top_k_context']\n new_prompt, chosen_context = self._build_prompt(mode, old_prompt, top_k_context)\n new_prompt_line = {\n 'prompt': new_prompt,\n 'metadata': task['metadata'],\n }\n new_prompt_line['metadata']['query_window'] = {\n 'context': query_line['context'],\n 'metadata': query_line['metadata'],\n }\n new_prompt_line['metadata']['top_k_context'] = [\n {\n 'context': x[0]['context'],\n 'metadata': x[0]['metadata'],\n 'sim_score': x[1],\n } for x in chosen_context\n ]\n new_prompt_line['metadata']['window_size'] = query_line['metadata']['window_size']\n new_prompt_line['metadata']['slice_size'] = chosen_context[0][0]['metadata'][0]['slice_size']\n new_prompt_lines.append(new_prompt_line)\n print('done! ' + self.log_message)\n return new_prompt_lines\n\nclass BuildPromptWrapper:\n def __init__(self, vectorizer, benchmark, repos, window_size, slice_size, tokenizer):\n if vectorizer == 'one-gram':\n self.vector_path_builder = FilePathBuilder.one_gram_vector_path\n elif vectorizer == 'ada002':\n self.vector_path_builder = FilePathBuilder.ada002_vector_path\n self.max_top_k = 20\n self.repos = repos\n self.window_size = window_size\n self.slice_size = slice_size\n if benchmark == CONSTANTS.line_benchmark:\n self.task_path = FilePathBuilder.random_line_completion_benchmark\n elif benchmark == CONSTANTS.api_benchmark:\n self.task_path = FilePathBuilder.api_completion_benchmark\n elif benchmark == CONSTANTS.short_api_benchmark:\n self.task_path = FilePathBuilder.short_api_completion_benchmark\n elif benchmark == CONSTANTS.short_line_benchmark:\n self.task_path = FilePathBuilder.short_random_line_completion_benchmark\n self.benchmark = benchmark","source_hash":"1907f0b26ba4fbd02b1b43cf88c0478b8ee9cad7e6fe15e5c769a8863c10039b","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_prompt.BuildPromptWrapper","uri":"program://CodeT/class/RepoCoder.build_prompt.BuildPromptWrapper#L115-L161","kind":"class","name":"BuildPromptWrapper","path":"RepoCoder/build_prompt.py","language":"python","start_line":115,"end_line":161,"context_start_line":95,"context_end_line":162,"code":" 'prompt': new_prompt,\n 'metadata': task['metadata'],\n }\n new_prompt_line['metadata']['query_window'] = {\n 'context': query_line['context'],\n 'metadata': query_line['metadata'],\n }\n new_prompt_line['metadata']['top_k_context'] = [\n {\n 'context': x[0]['context'],\n 'metadata': x[0]['metadata'],\n 'sim_score': x[1],\n } for x in chosen_context\n ]\n new_prompt_line['metadata']['window_size'] = query_line['metadata']['window_size']\n new_prompt_line['metadata']['slice_size'] = chosen_context[0][0]['metadata'][0]['slice_size']\n new_prompt_lines.append(new_prompt_line)\n print('done! ' + self.log_message)\n return new_prompt_lines\n\nclass BuildPromptWrapper:\n def __init__(self, vectorizer, benchmark, repos, window_size, slice_size, tokenizer):\n if vectorizer == 'one-gram':\n self.vector_path_builder = FilePathBuilder.one_gram_vector_path\n elif vectorizer == 'ada002':\n self.vector_path_builder = FilePathBuilder.ada002_vector_path\n self.max_top_k = 20\n self.repos = repos\n self.window_size = window_size\n self.slice_size = slice_size\n if benchmark == CONSTANTS.line_benchmark:\n self.task_path = FilePathBuilder.random_line_completion_benchmark\n elif benchmark == CONSTANTS.api_benchmark:\n self.task_path = FilePathBuilder.api_completion_benchmark\n elif benchmark == CONSTANTS.short_api_benchmark:\n self.task_path = FilePathBuilder.short_api_completion_benchmark\n elif benchmark == CONSTANTS.short_line_benchmark:\n self.task_path = FilePathBuilder.short_random_line_completion_benchmark\n self.benchmark = benchmark\n self.tokenizer = tokenizer\n \n def _run(self, mode, query_window_path_builder, output_file_path):\n workers = []\n for repo in self.repos:\n query_window_path = query_window_path_builder(repo, self.window_size)\n query_line_path = self.vector_path_builder(query_window_path)\n repo_window_path = FilePathBuilder.repo_windows_path(repo, self.window_size, self.slice_size)\n repo_embedding_path = self.vector_path_builder(repo_window_path)\n retrieval_results = FilePathBuilder.retrieval_results_path(query_line_path, repo_embedding_path, self.max_top_k)\n \n query_lines_with_retrieval_results = Tools.load_pickle(retrieval_results)\n log_message = f'repo: {repo}, window: {self.window_size}, slice: {self.slice_size}'\n worker = PromptBuilder(query_lines_with_retrieval_results, self.task_path, log_message, self.tokenizer)\n workers.append(worker)\n lines = []\n for worker in workers:\n lines += worker.build_2nd_stage_input_file(mode)\n Tools.dump_jsonl(lines, output_file_path)\n\n def build_first_search_prompt(self, mode, output_path):\n query_line_path_temp = functools.partial(FilePathBuilder.search_first_window_path, self.benchmark, mode)\n self._run(mode, query_line_path_temp, output_path)\n\n \n def build_prediction_prompt(self, mode, prediction_path, output_path):\n query_line_path_temp = functools.partial(FilePathBuilder.gen_first_window_path, self.benchmark, mode, prediction_path)\n self._run(mode, query_line_path_temp, output_path)\n","source_hash":"1907f0b26ba4fbd02b1b43cf88c0478b8ee9cad7e6fe15e5c769a8863c10039b","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_prompt.__init__","uri":"program://CodeT/function/RepoCoder.build_prompt.__init__#L116-L134","kind":"function","name":"__init__","path":"RepoCoder/build_prompt.py","language":"python","start_line":116,"end_line":134,"context_start_line":96,"context_end_line":154,"code":" 'metadata': task['metadata'],\n }\n new_prompt_line['metadata']['query_window'] = {\n 'context': query_line['context'],\n 'metadata': query_line['metadata'],\n }\n new_prompt_line['metadata']['top_k_context'] = [\n {\n 'context': x[0]['context'],\n 'metadata': x[0]['metadata'],\n 'sim_score': x[1],\n } for x in chosen_context\n ]\n new_prompt_line['metadata']['window_size'] = query_line['metadata']['window_size']\n new_prompt_line['metadata']['slice_size'] = chosen_context[0][0]['metadata'][0]['slice_size']\n new_prompt_lines.append(new_prompt_line)\n print('done! ' + self.log_message)\n return new_prompt_lines\n\nclass BuildPromptWrapper:\n def __init__(self, vectorizer, benchmark, repos, window_size, slice_size, tokenizer):\n if vectorizer == 'one-gram':\n self.vector_path_builder = FilePathBuilder.one_gram_vector_path\n elif vectorizer == 'ada002':\n self.vector_path_builder = FilePathBuilder.ada002_vector_path\n self.max_top_k = 20\n self.repos = repos\n self.window_size = window_size\n self.slice_size = slice_size\n if benchmark == CONSTANTS.line_benchmark:\n self.task_path = FilePathBuilder.random_line_completion_benchmark\n elif benchmark == CONSTANTS.api_benchmark:\n self.task_path = FilePathBuilder.api_completion_benchmark\n elif benchmark == CONSTANTS.short_api_benchmark:\n self.task_path = FilePathBuilder.short_api_completion_benchmark\n elif benchmark == CONSTANTS.short_line_benchmark:\n self.task_path = FilePathBuilder.short_random_line_completion_benchmark\n self.benchmark = benchmark\n self.tokenizer = tokenizer\n \n def _run(self, mode, query_window_path_builder, output_file_path):\n workers = []\n for repo in self.repos:\n query_window_path = query_window_path_builder(repo, self.window_size)\n query_line_path = self.vector_path_builder(query_window_path)\n repo_window_path = FilePathBuilder.repo_windows_path(repo, self.window_size, self.slice_size)\n repo_embedding_path = self.vector_path_builder(repo_window_path)\n retrieval_results = FilePathBuilder.retrieval_results_path(query_line_path, repo_embedding_path, self.max_top_k)\n \n query_lines_with_retrieval_results = Tools.load_pickle(retrieval_results)\n log_message = f'repo: {repo}, window: {self.window_size}, slice: {self.slice_size}'\n worker = PromptBuilder(query_lines_with_retrieval_results, self.task_path, log_message, self.tokenizer)\n workers.append(worker)\n lines = []\n for worker in workers:\n lines += worker.build_2nd_stage_input_file(mode)\n Tools.dump_jsonl(lines, output_file_path)\n\n def build_first_search_prompt(self, mode, output_path):","source_hash":"1907f0b26ba4fbd02b1b43cf88c0478b8ee9cad7e6fe15e5c769a8863c10039b","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_prompt._make_a_block","uri":"program://CodeT/function/RepoCoder.build_prompt._make_a_block#L24-L40","kind":"function","name":"_make_a_block","path":"RepoCoder/build_prompt.py","language":"python","start_line":24,"end_line":40,"context_start_line":4,"context_end_line":60,"code":"import functools\nimport os\n\nfrom utils import Tools, FilePathBuilder, CodexTokenizer, CodeGenTokenizer, CONSTANTS\n\nclass PromptBuilder:\n def __init__(self, query_lines_with_retrieval_results, task_path, log_message, tokenizer):\n self.query_lines_with_retrieval_results = query_lines_with_retrieval_results\n self.log_message = log_message\n if tokenizer == CodexTokenizer:\n self.tokenizer = CodexTokenizer()\n self.max_retrieval_length = 2000 # half of the max length of the model\n elif tokenizer == CodeGenTokenizer:\n self.tokenizer = CodeGenTokenizer()\n self.max_retrieval_length = 1000\n tasks = Tools.load_jsonl(task_path)\n self.tasks_by_task_id = {task['metadata']['task_id']: task for task in tasks}\n self.seperator = '# ' + '-' * 50\n self.max_examples = 10 # maximum number of examples to be included in the prompt\n\n def _make_a_block(self, retrieved_context):\n content, sim_score = retrieved_context\n metadata = content['metadata']\n # put the file path in the comment\n assert metadata[0]['fpath_tuple'][0] == metadata[0]['repo']\n f_paths = ['/'.join(x['fpath_tuple'][1:]) for x in metadata]\n f_paths_str = '\\n'.join([f'# {f_path}' for f_path in f_paths])\n f_path_comment = f'# the below code fragment can be found in:'\n # put code lines in the comment\n content_lines = content['context'].splitlines()\n content_lines_comment = [f'# {line}' for line in content_lines]\n # aggregate the comment and the code lines\n \n block_str = '\\n'.join([f_path_comment, f_paths_str, self.seperator] + content_lines_comment + [self.seperator]) + '\\n'\n tokenized_block = self.tokenizer.tokenize(block_str)\n token_len = len(tokenized_block)\n return block_str, token_len\n\n def _make_an_extended_block(self, retrieved_context):\n content, sim_score = retrieved_context\n metadata = content['metadata']\n # put the file path in the comment\n assert metadata[0]['fpath_tuple'][0] == metadata[0]['repo']\n f_paths = ['/'.join(x['fpath_tuple'][1:]) for x in metadata]\n f_paths_str = '\\n'.join([f'# {f_path}' for f_path in f_paths])\n f_path_comment = f'# the below code fragment can be found in:'\n # put code lines in the comment\n original_code = Tools.read_code(os.path.join(FilePathBuilder.repo_base_dir, *metadata[0]['fpath_tuple']))\n code_lines = original_code.splitlines()\n end_line_no = metadata[0]['end_line_no']\n window_size = metadata[0]['window_size']\n slice_size = metadata[0]['slice_size']\n new_end_line_no = min(end_line_no + window_size // slice_size, len(code_lines))\n new_start_line_no = max(0, new_end_line_no - window_size)\n content_lines = code_lines[new_start_line_no:new_end_line_no]\n content_lines_comment = [f'# {line}' for line in content_lines]\n # aggregate the comment and the code lines","source_hash":"1907f0b26ba4fbd02b1b43cf88c0478b8ee9cad7e6fe15e5c769a8863c10039b","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_prompt._make_an_extended_block","uri":"program://CodeT/function/RepoCoder.build_prompt._make_an_extended_block#L42-L64","kind":"function","name":"_make_an_extended_block","path":"RepoCoder/build_prompt.py","language":"python","start_line":42,"end_line":64,"context_start_line":22,"context_end_line":84,"code":" self.max_examples = 10 # maximum number of examples to be included in the prompt\n\n def _make_a_block(self, retrieved_context):\n content, sim_score = retrieved_context\n metadata = content['metadata']\n # put the file path in the comment\n assert metadata[0]['fpath_tuple'][0] == metadata[0]['repo']\n f_paths = ['/'.join(x['fpath_tuple'][1:]) for x in metadata]\n f_paths_str = '\\n'.join([f'# {f_path}' for f_path in f_paths])\n f_path_comment = f'# the below code fragment can be found in:'\n # put code lines in the comment\n content_lines = content['context'].splitlines()\n content_lines_comment = [f'# {line}' for line in content_lines]\n # aggregate the comment and the code lines\n \n block_str = '\\n'.join([f_path_comment, f_paths_str, self.seperator] + content_lines_comment + [self.seperator]) + '\\n'\n tokenized_block = self.tokenizer.tokenize(block_str)\n token_len = len(tokenized_block)\n return block_str, token_len\n\n def _make_an_extended_block(self, retrieved_context):\n content, sim_score = retrieved_context\n metadata = content['metadata']\n # put the file path in the comment\n assert metadata[0]['fpath_tuple'][0] == metadata[0]['repo']\n f_paths = ['/'.join(x['fpath_tuple'][1:]) for x in metadata]\n f_paths_str = '\\n'.join([f'# {f_path}' for f_path in f_paths])\n f_path_comment = f'# the below code fragment can be found in:'\n # put code lines in the comment\n original_code = Tools.read_code(os.path.join(FilePathBuilder.repo_base_dir, *metadata[0]['fpath_tuple']))\n code_lines = original_code.splitlines()\n end_line_no = metadata[0]['end_line_no']\n window_size = metadata[0]['window_size']\n slice_size = metadata[0]['slice_size']\n new_end_line_no = min(end_line_no + window_size // slice_size, len(code_lines))\n new_start_line_no = max(0, new_end_line_no - window_size)\n content_lines = code_lines[new_start_line_no:new_end_line_no]\n content_lines_comment = [f'# {line}' for line in content_lines]\n # aggregate the comment and the code lines\n block_str = '\\n'.join([f_path_comment, f_paths_str, self.seperator] + content_lines_comment + [self.seperator]) + '\\n'\n tokenized_block = self.tokenizer.tokenize(block_str)\n token_len = len(tokenized_block)\n return block_str, token_len\n\n def _build_prompt(self, mode, prompt, top_k_context):\n prepend_context = \"# Here are some relevant code fragments from other files of the repo:\\n\"\n prepend_context += self.seperator + '\\n'\n current_token_length = 20 # the length of the head_prompt, same for codex and codegen tokenizer\n prepend_blocks = []\n chosen_context = []\n make_block_func = self._make_an_extended_block if mode == CONSTANTS.rg else self._make_a_block\n for retrieved_context in top_k_context[::-1]:\n if len(chosen_context) >= self.max_examples:\n break\n block_str, token_len = make_block_func(retrieved_context)\n if current_token_length + token_len < self.max_retrieval_length:\n prepend_blocks.insert(0, block_str) \n current_token_length += token_len\n chosen_context.append(retrieved_context)\n else:\n continue\n prepend_context += ''.join(prepend_blocks) # all the blocks already have a line break at the end\n return prepend_context + '\\n' + prompt, chosen_context","source_hash":"1907f0b26ba4fbd02b1b43cf88c0478b8ee9cad7e6fe15e5c769a8863c10039b","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_prompt._build_prompt","uri":"program://CodeT/function/RepoCoder.build_prompt._build_prompt#L66-L84","kind":"function","name":"_build_prompt","path":"RepoCoder/build_prompt.py","language":"python","start_line":66,"end_line":84,"context_start_line":46,"context_end_line":104,"code":" assert metadata[0]['fpath_tuple'][0] == metadata[0]['repo']\n f_paths = ['/'.join(x['fpath_tuple'][1:]) for x in metadata]\n f_paths_str = '\\n'.join([f'# {f_path}' for f_path in f_paths])\n f_path_comment = f'# the below code fragment can be found in:'\n # put code lines in the comment\n original_code = Tools.read_code(os.path.join(FilePathBuilder.repo_base_dir, *metadata[0]['fpath_tuple']))\n code_lines = original_code.splitlines()\n end_line_no = metadata[0]['end_line_no']\n window_size = metadata[0]['window_size']\n slice_size = metadata[0]['slice_size']\n new_end_line_no = min(end_line_no + window_size // slice_size, len(code_lines))\n new_start_line_no = max(0, new_end_line_no - window_size)\n content_lines = code_lines[new_start_line_no:new_end_line_no]\n content_lines_comment = [f'# {line}' for line in content_lines]\n # aggregate the comment and the code lines\n block_str = '\\n'.join([f_path_comment, f_paths_str, self.seperator] + content_lines_comment + [self.seperator]) + '\\n'\n tokenized_block = self.tokenizer.tokenize(block_str)\n token_len = len(tokenized_block)\n return block_str, token_len\n\n def _build_prompt(self, mode, prompt, top_k_context):\n prepend_context = \"# Here are some relevant code fragments from other files of the repo:\\n\"\n prepend_context += self.seperator + '\\n'\n current_token_length = 20 # the length of the head_prompt, same for codex and codegen tokenizer\n prepend_blocks = []\n chosen_context = []\n make_block_func = self._make_an_extended_block if mode == CONSTANTS.rg else self._make_a_block\n for retrieved_context in top_k_context[::-1]:\n if len(chosen_context) >= self.max_examples:\n break\n block_str, token_len = make_block_func(retrieved_context)\n if current_token_length + token_len < self.max_retrieval_length:\n prepend_blocks.insert(0, block_str) \n current_token_length += token_len\n chosen_context.append(retrieved_context)\n else:\n continue\n prepend_context += ''.join(prepend_blocks) # all the blocks already have a line break at the end\n return prepend_context + '\\n' + prompt, chosen_context\n\n def build_2nd_stage_input_file(self, mode):\n new_prompt_lines = []\n for query_line in self.query_lines_with_retrieval_results:\n task_id = query_line['metadata']['task_id']\n task = self.tasks_by_task_id[task_id]\n old_prompt = task['prompt']\n top_k_context = query_line['top_k_context']\n new_prompt, chosen_context = self._build_prompt(mode, old_prompt, top_k_context)\n new_prompt_line = {\n 'prompt': new_prompt,\n 'metadata': task['metadata'],\n }\n new_prompt_line['metadata']['query_window'] = {\n 'context': query_line['context'],\n 'metadata': query_line['metadata'],\n }\n new_prompt_line['metadata']['top_k_context'] = [\n {\n 'context': x[0]['context'],","source_hash":"1907f0b26ba4fbd02b1b43cf88c0478b8ee9cad7e6fe15e5c769a8863c10039b","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_prompt.build_2nd_stage_input_file","uri":"program://CodeT/function/RepoCoder.build_prompt.build_2nd_stage_input_file#L86-L113","kind":"function","name":"build_2nd_stage_input_file","path":"RepoCoder/build_prompt.py","language":"python","start_line":86,"end_line":113,"context_start_line":66,"context_end_line":133,"code":" def _build_prompt(self, mode, prompt, top_k_context):\n prepend_context = \"# Here are some relevant code fragments from other files of the repo:\\n\"\n prepend_context += self.seperator + '\\n'\n current_token_length = 20 # the length of the head_prompt, same for codex and codegen tokenizer\n prepend_blocks = []\n chosen_context = []\n make_block_func = self._make_an_extended_block if mode == CONSTANTS.rg else self._make_a_block\n for retrieved_context in top_k_context[::-1]:\n if len(chosen_context) >= self.max_examples:\n break\n block_str, token_len = make_block_func(retrieved_context)\n if current_token_length + token_len < self.max_retrieval_length:\n prepend_blocks.insert(0, block_str) \n current_token_length += token_len\n chosen_context.append(retrieved_context)\n else:\n continue\n prepend_context += ''.join(prepend_blocks) # all the blocks already have a line break at the end\n return prepend_context + '\\n' + prompt, chosen_context\n\n def build_2nd_stage_input_file(self, mode):\n new_prompt_lines = []\n for query_line in self.query_lines_with_retrieval_results:\n task_id = query_line['metadata']['task_id']\n task = self.tasks_by_task_id[task_id]\n old_prompt = task['prompt']\n top_k_context = query_line['top_k_context']\n new_prompt, chosen_context = self._build_prompt(mode, old_prompt, top_k_context)\n new_prompt_line = {\n 'prompt': new_prompt,\n 'metadata': task['metadata'],\n }\n new_prompt_line['metadata']['query_window'] = {\n 'context': query_line['context'],\n 'metadata': query_line['metadata'],\n }\n new_prompt_line['metadata']['top_k_context'] = [\n {\n 'context': x[0]['context'],\n 'metadata': x[0]['metadata'],\n 'sim_score': x[1],\n } for x in chosen_context\n ]\n new_prompt_line['metadata']['window_size'] = query_line['metadata']['window_size']\n new_prompt_line['metadata']['slice_size'] = chosen_context[0][0]['metadata'][0]['slice_size']\n new_prompt_lines.append(new_prompt_line)\n print('done! ' + self.log_message)\n return new_prompt_lines\n\nclass BuildPromptWrapper:\n def __init__(self, vectorizer, benchmark, repos, window_size, slice_size, tokenizer):\n if vectorizer == 'one-gram':\n self.vector_path_builder = FilePathBuilder.one_gram_vector_path\n elif vectorizer == 'ada002':\n self.vector_path_builder = FilePathBuilder.ada002_vector_path\n self.max_top_k = 20\n self.repos = repos\n self.window_size = window_size\n self.slice_size = slice_size\n if benchmark == CONSTANTS.line_benchmark:\n self.task_path = FilePathBuilder.random_line_completion_benchmark\n elif benchmark == CONSTANTS.api_benchmark:\n self.task_path = FilePathBuilder.api_completion_benchmark\n elif benchmark == CONSTANTS.short_api_benchmark:\n self.task_path = FilePathBuilder.short_api_completion_benchmark\n elif benchmark == CONSTANTS.short_line_benchmark:\n self.task_path = FilePathBuilder.short_random_line_completion_benchmark\n self.benchmark = benchmark","source_hash":"1907f0b26ba4fbd02b1b43cf88c0478b8ee9cad7e6fe15e5c769a8863c10039b","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_prompt._run","uri":"program://CodeT/function/RepoCoder.build_prompt._run#L136-L152","kind":"function","name":"_run","path":"RepoCoder/build_prompt.py","language":"python","start_line":136,"end_line":152,"context_start_line":116,"context_end_line":162,"code":" def __init__(self, vectorizer, benchmark, repos, window_size, slice_size, tokenizer):\n if vectorizer == 'one-gram':\n self.vector_path_builder = FilePathBuilder.one_gram_vector_path\n elif vectorizer == 'ada002':\n self.vector_path_builder = FilePathBuilder.ada002_vector_path\n self.max_top_k = 20\n self.repos = repos\n self.window_size = window_size\n self.slice_size = slice_size\n if benchmark == CONSTANTS.line_benchmark:\n self.task_path = FilePathBuilder.random_line_completion_benchmark\n elif benchmark == CONSTANTS.api_benchmark:\n self.task_path = FilePathBuilder.api_completion_benchmark\n elif benchmark == CONSTANTS.short_api_benchmark:\n self.task_path = FilePathBuilder.short_api_completion_benchmark\n elif benchmark == CONSTANTS.short_line_benchmark:\n self.task_path = FilePathBuilder.short_random_line_completion_benchmark\n self.benchmark = benchmark\n self.tokenizer = tokenizer\n \n def _run(self, mode, query_window_path_builder, output_file_path):\n workers = []\n for repo in self.repos:\n query_window_path = query_window_path_builder(repo, self.window_size)\n query_line_path = self.vector_path_builder(query_window_path)\n repo_window_path = FilePathBuilder.repo_windows_path(repo, self.window_size, self.slice_size)\n repo_embedding_path = self.vector_path_builder(repo_window_path)\n retrieval_results = FilePathBuilder.retrieval_results_path(query_line_path, repo_embedding_path, self.max_top_k)\n \n query_lines_with_retrieval_results = Tools.load_pickle(retrieval_results)\n log_message = f'repo: {repo}, window: {self.window_size}, slice: {self.slice_size}'\n worker = PromptBuilder(query_lines_with_retrieval_results, self.task_path, log_message, self.tokenizer)\n workers.append(worker)\n lines = []\n for worker in workers:\n lines += worker.build_2nd_stage_input_file(mode)\n Tools.dump_jsonl(lines, output_file_path)\n\n def build_first_search_prompt(self, mode, output_path):\n query_line_path_temp = functools.partial(FilePathBuilder.search_first_window_path, self.benchmark, mode)\n self._run(mode, query_line_path_temp, output_path)\n\n \n def build_prediction_prompt(self, mode, prediction_path, output_path):\n query_line_path_temp = functools.partial(FilePathBuilder.gen_first_window_path, self.benchmark, mode, prediction_path)\n self._run(mode, query_line_path_temp, output_path)\n","source_hash":"1907f0b26ba4fbd02b1b43cf88c0478b8ee9cad7e6fe15e5c769a8863c10039b","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_prompt.build_first_search_prompt","uri":"program://CodeT/function/RepoCoder.build_prompt.build_first_search_prompt#L154-L156","kind":"function","name":"build_first_search_prompt","path":"RepoCoder/build_prompt.py","language":"python","start_line":154,"end_line":156,"context_start_line":134,"context_end_line":162,"code":" self.tokenizer = tokenizer\n \n def _run(self, mode, query_window_path_builder, output_file_path):\n workers = []\n for repo in self.repos:\n query_window_path = query_window_path_builder(repo, self.window_size)\n query_line_path = self.vector_path_builder(query_window_path)\n repo_window_path = FilePathBuilder.repo_windows_path(repo, self.window_size, self.slice_size)\n repo_embedding_path = self.vector_path_builder(repo_window_path)\n retrieval_results = FilePathBuilder.retrieval_results_path(query_line_path, repo_embedding_path, self.max_top_k)\n \n query_lines_with_retrieval_results = Tools.load_pickle(retrieval_results)\n log_message = f'repo: {repo}, window: {self.window_size}, slice: {self.slice_size}'\n worker = PromptBuilder(query_lines_with_retrieval_results, self.task_path, log_message, self.tokenizer)\n workers.append(worker)\n lines = []\n for worker in workers:\n lines += worker.build_2nd_stage_input_file(mode)\n Tools.dump_jsonl(lines, output_file_path)\n\n def build_first_search_prompt(self, mode, output_path):\n query_line_path_temp = functools.partial(FilePathBuilder.search_first_window_path, self.benchmark, mode)\n self._run(mode, query_line_path_temp, output_path)\n\n \n def build_prediction_prompt(self, mode, prediction_path, output_path):\n query_line_path_temp = functools.partial(FilePathBuilder.gen_first_window_path, self.benchmark, mode, prediction_path)\n self._run(mode, query_line_path_temp, output_path)\n","source_hash":"1907f0b26ba4fbd02b1b43cf88c0478b8ee9cad7e6fe15e5c769a8863c10039b","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.build_prompt.build_prediction_prompt","uri":"program://CodeT/function/RepoCoder.build_prompt.build_prediction_prompt#L159-L161","kind":"function","name":"build_prediction_prompt","path":"RepoCoder/build_prompt.py","language":"python","start_line":159,"end_line":161,"context_start_line":139,"context_end_line":162,"code":" query_window_path = query_window_path_builder(repo, self.window_size)\n query_line_path = self.vector_path_builder(query_window_path)\n repo_window_path = FilePathBuilder.repo_windows_path(repo, self.window_size, self.slice_size)\n repo_embedding_path = self.vector_path_builder(repo_window_path)\n retrieval_results = FilePathBuilder.retrieval_results_path(query_line_path, repo_embedding_path, self.max_top_k)\n \n query_lines_with_retrieval_results = Tools.load_pickle(retrieval_results)\n log_message = f'repo: {repo}, window: {self.window_size}, slice: {self.slice_size}'\n worker = PromptBuilder(query_lines_with_retrieval_results, self.task_path, log_message, self.tokenizer)\n workers.append(worker)\n lines = []\n for worker in workers:\n lines += worker.build_2nd_stage_input_file(mode)\n Tools.dump_jsonl(lines, output_file_path)\n\n def build_first_search_prompt(self, mode, output_path):\n query_line_path_temp = functools.partial(FilePathBuilder.search_first_window_path, self.benchmark, mode)\n self._run(mode, query_line_path_temp, output_path)\n\n \n def build_prediction_prompt(self, mode, prediction_path, output_path):\n query_line_path_temp = functools.partial(FilePathBuilder.gen_first_window_path, self.benchmark, mode, prediction_path)\n self._run(mode, query_line_path_temp, output_path)\n","source_hash":"1907f0b26ba4fbd02b1b43cf88c0478b8ee9cad7e6fe15e5c769a8863c10039b","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.make_window","uri":"program://CodeT/module/RepoCoder.make_window#L1-L229","kind":"module","name":"RepoCoder.make_window","path":"RepoCoder/make_window.py","language":"python","start_line":1,"end_line":229,"context_start_line":1,"context_end_line":229,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport itertools\nimport functools\n\nfrom utils import Tools, FilePathBuilder, CONSTANTS\nfrom collections import defaultdict\n\nclass RepoWindowMaker:\n def __init__(self, repo, window_size, slice_size):\n self.repo = repo\n self.window_size = window_size\n self.slice_size = slice_size\n self.slice_step = 1 if window_size // slice_size == 0 else window_size // slice_size\n self.source_code_files = Tools.iterate_repository(repo)\n \n def _buid_windows_for_a_file(self, fpath_tuple, code):\n code_windows = []\n code_lines = code.splitlines()\n delta_size = self.window_size // 2\n for line_no in range(0, len(code_lines), self.slice_step): # line_no starts from 0\n start_line_no = max(0, line_no - delta_size)\n end_line_no = min(len(code_lines), line_no + self.window_size - delta_size)\n window_lines = [i for i in code_lines[start_line_no:end_line_no]]\n if not window_lines: # all empty lines\n continue\n window_text = '\\n'.join(window_lines)\n code_windows.append({\n 'context': window_text,\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no,\n 'start_line_no': start_line_no,\n 'end_line_no': end_line_no,\n 'window_size': self.window_size,\n 'repo': self.repo,\n 'slice_size': self.slice_size,\n }\n })\n return code_windows\n \n def _merge_windows_with_same_context(self, code_windows):\n merged_code_windows = defaultdict(list)\n for code_window in code_windows:\n context = code_window['context']\n metadata = code_window['metadata']\n merged_code_windows[context].append(metadata)\n json_lines = []\n for context, metadata_list in merged_code_windows.items():\n json_lines.append({\n 'context': context,\n 'metadata': metadata_list\n })\n return json_lines\n\n def build_windows(self):\n all_code_windows = []\n for fpath_tuple, code in self.source_code_files.items():\n all_code_windows += self._buid_windows_for_a_file(fpath_tuple, code)\n merged_code_windows = self._merge_windows_with_same_context(all_code_windows)\n print(f'build {len(merged_code_windows)} windows for {self.repo} with window size {self.window_size} and slice {self.slice_size}')\n output_path = FilePathBuilder.repo_windows_path(self.repo, self.window_size, self.slice_size)\n Tools.dump_pickle(merged_code_windows, output_path)\n\n\nclass BaselineWindowMaker:\n '''the retrieve-and-generate approach'''\n def __init__(self, benchmark, repo, window_size, tasks):\n self.benchmark = benchmark\n self.repo = repo\n self.window_size = window_size\n self.tasks = tasks\n self.source_code = Tools.iterate_repository(repo)\n \n def build_window(self):\n code_windows = []\n for task in self.tasks:\n if task['metadata']['task_id'].split('/')[0] != self.repo:\n continue\n fpath_tuple = tuple(task['metadata']['fpath_tuple'])\n line_no = task['metadata']['line_no']\n original_code = self.source_code[fpath_tuple]\n code_lines = original_code.splitlines()\n context_start_lineno = task['metadata']['context_start_lineno']\n start_line_no = max(context_start_lineno, line_no - self.window_size)\n window_lines = [i for i in code_lines[start_line_no:line_no]]\n code_windows.append({\n 'context': '\\n'.join(window_lines),\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no, # line_no starts from 0\n 'task_id': task['metadata']['task_id'],\n 'start_line_no': start_line_no,\n 'end_line_no': line_no,\n 'window_size': self.window_size,\n 'context_start_lineno': context_start_lineno,\n 'repo': self.repo\n }\n })\n print(f'build {len(code_windows)} baseline windows for {self.repo} with window size {self.window_size}')\n output_path = FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.rg, self.repo, self.window_size)\n Tools.dump_pickle(code_windows, output_path)\n\nclass GroundTruthWindowMaker:\n def __init__(self, benchmark, repo, window_size, tasks):\n self.benchmark = benchmark\n self.repo = repo\n self.window_size = window_size\n self.tasks = tasks\n self.source_code = Tools.iterate_repository(repo)\n\n def build_window(self):\n code_windows = []\n delta_size = self.window_size // 2\n for task in self.tasks:\n if task['metadata']['task_id'].split('/')[0] != self.repo:\n continue\n fpath_tuple = tuple(task['metadata']['fpath_tuple'])\n line_no = task['metadata']['line_no']\n original_code = self.source_code[fpath_tuple]\n code_lines = original_code.splitlines()\n context_start_lineno = task['metadata']['context_start_lineno']\n start_line_no = max(context_start_lineno, line_no - delta_size)\n end_line_no = min(len(code_lines), line_no + self.window_size - delta_size)\n window_lines = [i for i in code_lines[start_line_no:end_line_no]]\n code_windows.append({\n 'context': '\\n'.join(window_lines),\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no, # line_no starts from 0\n 'task_id': task['metadata']['task_id'],\n 'start_line_no': start_line_no,\n 'end_line_no': end_line_no,\n 'window_size': self.window_size,\n 'context_start_lineno': context_start_lineno,\n 'repo': self.repo\n }\n })\n print(f'build {len(code_windows)} ground truth windows for {self.repo} with window size {self.window_size}')\n output_path = FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.rg, self.repo, self.window_size)\n Tools.dump_pickle(code_windows, output_path)\n\nclass PredictionWindowMaker:\n def __init__(self, repo, window_size, prediction_path, window_path_builder):\n self.repo = repo\n self.window_size = window_size\n self.prediction_path = prediction_path\n self.source_code = Tools.iterate_repository(repo)\n self.predictions = Tools.load_jsonl(prediction_path)\n self.window_path_builder = window_path_builder\n \n def build_window(self, type='centered'):\n code_windows = []\n delta_size = self.window_size // 2\n for prediction in self.predictions:\n if prediction['metadata']['task_id'].split('/')[0] != self.repo:\n continue\n fpath_tuple = tuple(prediction['metadata']['fpath_tuple'])\n line_no = prediction['metadata']['line_no'] # line_no in prediction file starts from 0\n original_code = self.source_code[fpath_tuple]\n code_lines = original_code.splitlines()\n context_start_lineno = prediction['metadata']['context_start_lineno']\n start_line_no = max(context_start_lineno, line_no - delta_size)\n for sample in [prediction['choices'][i]['text'] for i in range(len(prediction['choices']))]:\n # TODO actually only one sample is generated\n sample_lines = [i for i in sample.splitlines() if i.strip()]\n new_code_lines = code_lines[:line_no] + sample_lines\n end_line_no = min(len(new_code_lines), line_no + self.window_size - delta_size)\n window_lines = [i for i in new_code_lines[start_line_no:end_line_no] if i.strip()]\n if not window_lines: # all empty lines\n continue\n code_windows.append({\n 'context': '\\n'.join(window_lines),\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no, # line_no starts from 0\n 'prediction': sample,\n 'task_id': prediction['metadata']['task_id'],\n 'start_line_no': start_line_no,\n 'end_line_no': end_line_no,\n 'window_size': self.window_size,\n 'context_start_lineno': context_start_lineno,\n 'repo': self.repo\n }\n })\n print(f'build {len(code_windows)} prediction windows for {self.repo} with window size {self.window_size}')\n output_path = self.window_path_builder(self.prediction_path, self.repo, self.window_size)\n Tools.dump_pickle(code_windows, output_path)\n\nclass MakeWindowWrapper:\n def __init__(self, benchmark, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n\n self.benchmark = benchmark\n\n if benchmark == CONSTANTS.line_benchmark:\n self.task_file_path = FilePathBuilder.random_line_completion_benchmark\n elif benchmark == CONSTANTS.api_benchmark:\n self.task_file_path = FilePathBuilder.api_completion_benchmark\n elif benchmark == CONSTANTS.short_line_benchmark:\n self.task_file_path = FilePathBuilder.short_random_line_completion_benchmark\n elif benchmark == CONSTANTS.short_api_benchmark:\n self.task_file_path = FilePathBuilder.short_api_completion_benchmark\n\n def window_for_repo_files(self):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n for repo in self.repos:\n repo_window_maker = RepoWindowMaker(repo, window_size, slice_size)\n repo_window_maker.build_windows()\n\n def window_for_baseline_and_ground(self):\n tasks = Tools.load_jsonl(self.task_file_path)\n for window_size in self.window_sizes:\n for repo in self.repos:\n baseline_window_maker = BaselineWindowMaker(self.benchmark, repo, window_size, tasks)\n ground_window_maker = GroundTruthWindowMaker(self.benchmark, repo, window_size, tasks)\n baseline_window_maker.build_window()\n ground_window_maker.build_window()\n\n def window_for_prediction(self, mode, prediction_path_template):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n prediction_path = prediction_path_template.format(window_size=window_size, slice_size=slice_size)\n for repo in self.repos:\n window_path_builder = functools.partial(FilePathBuilder.gen_first_window_path, self.benchmark, mode)\n pred_window_maker = PredictionWindowMaker(repo, window_size, prediction_path, window_path_builder)\n pred_window_maker.build_window()","source_hash":"45b01cfe027a887c0f3da521076a384b1577a1189ff7940842d706bb90c47603","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.make_window.RepoWindowMaker","uri":"program://CodeT/class/RepoCoder.make_window.RepoWindowMaker#L10-L64","kind":"class","name":"RepoWindowMaker","path":"RepoCoder/make_window.py","language":"python","start_line":10,"end_line":64,"context_start_line":1,"context_end_line":84,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport itertools\nimport functools\n\nfrom utils import Tools, FilePathBuilder, CONSTANTS\nfrom collections import defaultdict\n\nclass RepoWindowMaker:\n def __init__(self, repo, window_size, slice_size):\n self.repo = repo\n self.window_size = window_size\n self.slice_size = slice_size\n self.slice_step = 1 if window_size // slice_size == 0 else window_size // slice_size\n self.source_code_files = Tools.iterate_repository(repo)\n \n def _buid_windows_for_a_file(self, fpath_tuple, code):\n code_windows = []\n code_lines = code.splitlines()\n delta_size = self.window_size // 2\n for line_no in range(0, len(code_lines), self.slice_step): # line_no starts from 0\n start_line_no = max(0, line_no - delta_size)\n end_line_no = min(len(code_lines), line_no + self.window_size - delta_size)\n window_lines = [i for i in code_lines[start_line_no:end_line_no]]\n if not window_lines: # all empty lines\n continue\n window_text = '\\n'.join(window_lines)\n code_windows.append({\n 'context': window_text,\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no,\n 'start_line_no': start_line_no,\n 'end_line_no': end_line_no,\n 'window_size': self.window_size,\n 'repo': self.repo,\n 'slice_size': self.slice_size,\n }\n })\n return code_windows\n \n def _merge_windows_with_same_context(self, code_windows):\n merged_code_windows = defaultdict(list)\n for code_window in code_windows:\n context = code_window['context']\n metadata = code_window['metadata']\n merged_code_windows[context].append(metadata)\n json_lines = []\n for context, metadata_list in merged_code_windows.items():\n json_lines.append({\n 'context': context,\n 'metadata': metadata_list\n })\n return json_lines\n\n def build_windows(self):\n all_code_windows = []\n for fpath_tuple, code in self.source_code_files.items():\n all_code_windows += self._buid_windows_for_a_file(fpath_tuple, code)\n merged_code_windows = self._merge_windows_with_same_context(all_code_windows)\n print(f'build {len(merged_code_windows)} windows for {self.repo} with window size {self.window_size} and slice {self.slice_size}')\n output_path = FilePathBuilder.repo_windows_path(self.repo, self.window_size, self.slice_size)\n Tools.dump_pickle(merged_code_windows, output_path)\n\n\nclass BaselineWindowMaker:\n '''the retrieve-and-generate approach'''\n def __init__(self, benchmark, repo, window_size, tasks):\n self.benchmark = benchmark\n self.repo = repo\n self.window_size = window_size\n self.tasks = tasks\n self.source_code = Tools.iterate_repository(repo)\n \n def build_window(self):\n code_windows = []\n for task in self.tasks:\n if task['metadata']['task_id'].split('/')[0] != self.repo:\n continue\n fpath_tuple = tuple(task['metadata']['fpath_tuple'])\n line_no = task['metadata']['line_no']\n original_code = self.source_code[fpath_tuple]\n code_lines = original_code.splitlines()","source_hash":"45b01cfe027a887c0f3da521076a384b1577a1189ff7940842d706bb90c47603","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.make_window.BaselineWindowMaker","uri":"program://CodeT/class/RepoCoder.make_window.BaselineWindowMaker#L67-L103","kind":"class","name":"BaselineWindowMaker","path":"RepoCoder/make_window.py","language":"python","start_line":67,"end_line":103,"context_start_line":47,"context_end_line":123,"code":" metadata = code_window['metadata']\n merged_code_windows[context].append(metadata)\n json_lines = []\n for context, metadata_list in merged_code_windows.items():\n json_lines.append({\n 'context': context,\n 'metadata': metadata_list\n })\n return json_lines\n\n def build_windows(self):\n all_code_windows = []\n for fpath_tuple, code in self.source_code_files.items():\n all_code_windows += self._buid_windows_for_a_file(fpath_tuple, code)\n merged_code_windows = self._merge_windows_with_same_context(all_code_windows)\n print(f'build {len(merged_code_windows)} windows for {self.repo} with window size {self.window_size} and slice {self.slice_size}')\n output_path = FilePathBuilder.repo_windows_path(self.repo, self.window_size, self.slice_size)\n Tools.dump_pickle(merged_code_windows, output_path)\n\n\nclass BaselineWindowMaker:\n '''the retrieve-and-generate approach'''\n def __init__(self, benchmark, repo, window_size, tasks):\n self.benchmark = benchmark\n self.repo = repo\n self.window_size = window_size\n self.tasks = tasks\n self.source_code = Tools.iterate_repository(repo)\n \n def build_window(self):\n code_windows = []\n for task in self.tasks:\n if task['metadata']['task_id'].split('/')[0] != self.repo:\n continue\n fpath_tuple = tuple(task['metadata']['fpath_tuple'])\n line_no = task['metadata']['line_no']\n original_code = self.source_code[fpath_tuple]\n code_lines = original_code.splitlines()\n context_start_lineno = task['metadata']['context_start_lineno']\n start_line_no = max(context_start_lineno, line_no - self.window_size)\n window_lines = [i for i in code_lines[start_line_no:line_no]]\n code_windows.append({\n 'context': '\\n'.join(window_lines),\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no, # line_no starts from 0\n 'task_id': task['metadata']['task_id'],\n 'start_line_no': start_line_no,\n 'end_line_no': line_no,\n 'window_size': self.window_size,\n 'context_start_lineno': context_start_lineno,\n 'repo': self.repo\n }\n })\n print(f'build {len(code_windows)} baseline windows for {self.repo} with window size {self.window_size}')\n output_path = FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.rg, self.repo, self.window_size)\n Tools.dump_pickle(code_windows, output_path)\n\nclass GroundTruthWindowMaker:\n def __init__(self, benchmark, repo, window_size, tasks):\n self.benchmark = benchmark\n self.repo = repo\n self.window_size = window_size\n self.tasks = tasks\n self.source_code = Tools.iterate_repository(repo)\n\n def build_window(self):\n code_windows = []\n delta_size = self.window_size // 2\n for task in self.tasks:\n if task['metadata']['task_id'].split('/')[0] != self.repo:\n continue\n fpath_tuple = tuple(task['metadata']['fpath_tuple'])\n line_no = task['metadata']['line_no']\n original_code = self.source_code[fpath_tuple]\n code_lines = original_code.splitlines()\n context_start_lineno = task['metadata']['context_start_lineno']","source_hash":"45b01cfe027a887c0f3da521076a384b1577a1189ff7940842d706bb90c47603","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.make_window.GroundTruthWindowMaker","uri":"program://CodeT/class/RepoCoder.make_window.GroundTruthWindowMaker#L105-L142","kind":"class","name":"GroundTruthWindowMaker","path":"RepoCoder/make_window.py","language":"python","start_line":105,"end_line":142,"context_start_line":85,"context_end_line":162,"code":" context_start_lineno = task['metadata']['context_start_lineno']\n start_line_no = max(context_start_lineno, line_no - self.window_size)\n window_lines = [i for i in code_lines[start_line_no:line_no]]\n code_windows.append({\n 'context': '\\n'.join(window_lines),\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no, # line_no starts from 0\n 'task_id': task['metadata']['task_id'],\n 'start_line_no': start_line_no,\n 'end_line_no': line_no,\n 'window_size': self.window_size,\n 'context_start_lineno': context_start_lineno,\n 'repo': self.repo\n }\n })\n print(f'build {len(code_windows)} baseline windows for {self.repo} with window size {self.window_size}')\n output_path = FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.rg, self.repo, self.window_size)\n Tools.dump_pickle(code_windows, output_path)\n\nclass GroundTruthWindowMaker:\n def __init__(self, benchmark, repo, window_size, tasks):\n self.benchmark = benchmark\n self.repo = repo\n self.window_size = window_size\n self.tasks = tasks\n self.source_code = Tools.iterate_repository(repo)\n\n def build_window(self):\n code_windows = []\n delta_size = self.window_size // 2\n for task in self.tasks:\n if task['metadata']['task_id'].split('/')[0] != self.repo:\n continue\n fpath_tuple = tuple(task['metadata']['fpath_tuple'])\n line_no = task['metadata']['line_no']\n original_code = self.source_code[fpath_tuple]\n code_lines = original_code.splitlines()\n context_start_lineno = task['metadata']['context_start_lineno']\n start_line_no = max(context_start_lineno, line_no - delta_size)\n end_line_no = min(len(code_lines), line_no + self.window_size - delta_size)\n window_lines = [i for i in code_lines[start_line_no:end_line_no]]\n code_windows.append({\n 'context': '\\n'.join(window_lines),\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no, # line_no starts from 0\n 'task_id': task['metadata']['task_id'],\n 'start_line_no': start_line_no,\n 'end_line_no': end_line_no,\n 'window_size': self.window_size,\n 'context_start_lineno': context_start_lineno,\n 'repo': self.repo\n }\n })\n print(f'build {len(code_windows)} ground truth windows for {self.repo} with window size {self.window_size}')\n output_path = FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.rg, self.repo, self.window_size)\n Tools.dump_pickle(code_windows, output_path)\n\nclass PredictionWindowMaker:\n def __init__(self, repo, window_size, prediction_path, window_path_builder):\n self.repo = repo\n self.window_size = window_size\n self.prediction_path = prediction_path\n self.source_code = Tools.iterate_repository(repo)\n self.predictions = Tools.load_jsonl(prediction_path)\n self.window_path_builder = window_path_builder\n \n def build_window(self, type='centered'):\n code_windows = []\n delta_size = self.window_size // 2\n for prediction in self.predictions:\n if prediction['metadata']['task_id'].split('/')[0] != self.repo:\n continue\n fpath_tuple = tuple(prediction['metadata']['fpath_tuple'])\n line_no = prediction['metadata']['line_no'] # line_no in prediction file starts from 0\n original_code = self.source_code[fpath_tuple]\n code_lines = original_code.splitlines()","source_hash":"45b01cfe027a887c0f3da521076a384b1577a1189ff7940842d706bb90c47603","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.make_window.PredictionWindowMaker","uri":"program://CodeT/class/RepoCoder.make_window.PredictionWindowMaker#L144-L189","kind":"class","name":"PredictionWindowMaker","path":"RepoCoder/make_window.py","language":"python","start_line":144,"end_line":189,"context_start_line":124,"context_end_line":209,"code":" start_line_no = max(context_start_lineno, line_no - delta_size)\n end_line_no = min(len(code_lines), line_no + self.window_size - delta_size)\n window_lines = [i for i in code_lines[start_line_no:end_line_no]]\n code_windows.append({\n 'context': '\\n'.join(window_lines),\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no, # line_no starts from 0\n 'task_id': task['metadata']['task_id'],\n 'start_line_no': start_line_no,\n 'end_line_no': end_line_no,\n 'window_size': self.window_size,\n 'context_start_lineno': context_start_lineno,\n 'repo': self.repo\n }\n })\n print(f'build {len(code_windows)} ground truth windows for {self.repo} with window size {self.window_size}')\n output_path = FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.rg, self.repo, self.window_size)\n Tools.dump_pickle(code_windows, output_path)\n\nclass PredictionWindowMaker:\n def __init__(self, repo, window_size, prediction_path, window_path_builder):\n self.repo = repo\n self.window_size = window_size\n self.prediction_path = prediction_path\n self.source_code = Tools.iterate_repository(repo)\n self.predictions = Tools.load_jsonl(prediction_path)\n self.window_path_builder = window_path_builder\n \n def build_window(self, type='centered'):\n code_windows = []\n delta_size = self.window_size // 2\n for prediction in self.predictions:\n if prediction['metadata']['task_id'].split('/')[0] != self.repo:\n continue\n fpath_tuple = tuple(prediction['metadata']['fpath_tuple'])\n line_no = prediction['metadata']['line_no'] # line_no in prediction file starts from 0\n original_code = self.source_code[fpath_tuple]\n code_lines = original_code.splitlines()\n context_start_lineno = prediction['metadata']['context_start_lineno']\n start_line_no = max(context_start_lineno, line_no - delta_size)\n for sample in [prediction['choices'][i]['text'] for i in range(len(prediction['choices']))]:\n # TODO actually only one sample is generated\n sample_lines = [i for i in sample.splitlines() if i.strip()]\n new_code_lines = code_lines[:line_no] + sample_lines\n end_line_no = min(len(new_code_lines), line_no + self.window_size - delta_size)\n window_lines = [i for i in new_code_lines[start_line_no:end_line_no] if i.strip()]\n if not window_lines: # all empty lines\n continue\n code_windows.append({\n 'context': '\\n'.join(window_lines),\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no, # line_no starts from 0\n 'prediction': sample,\n 'task_id': prediction['metadata']['task_id'],\n 'start_line_no': start_line_no,\n 'end_line_no': end_line_no,\n 'window_size': self.window_size,\n 'context_start_lineno': context_start_lineno,\n 'repo': self.repo\n }\n })\n print(f'build {len(code_windows)} prediction windows for {self.repo} with window size {self.window_size}')\n output_path = self.window_path_builder(self.prediction_path, self.repo, self.window_size)\n Tools.dump_pickle(code_windows, output_path)\n\nclass MakeWindowWrapper:\n def __init__(self, benchmark, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n\n self.benchmark = benchmark\n\n if benchmark == CONSTANTS.line_benchmark:\n self.task_file_path = FilePathBuilder.random_line_completion_benchmark\n elif benchmark == CONSTANTS.api_benchmark:\n self.task_file_path = FilePathBuilder.api_completion_benchmark\n elif benchmark == CONSTANTS.short_line_benchmark:\n self.task_file_path = FilePathBuilder.short_random_line_completion_benchmark\n elif benchmark == CONSTANTS.short_api_benchmark:\n self.task_file_path = FilePathBuilder.short_api_completion_benchmark\n\n def window_for_repo_files(self):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):","source_hash":"45b01cfe027a887c0f3da521076a384b1577a1189ff7940842d706bb90c47603","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.make_window.MakeWindowWrapper","uri":"program://CodeT/class/RepoCoder.make_window.MakeWindowWrapper#L191-L229","kind":"class","name":"MakeWindowWrapper","path":"RepoCoder/make_window.py","language":"python","start_line":191,"end_line":229,"context_start_line":171,"context_end_line":229,"code":" if not window_lines: # all empty lines\n continue\n code_windows.append({\n 'context': '\\n'.join(window_lines),\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no, # line_no starts from 0\n 'prediction': sample,\n 'task_id': prediction['metadata']['task_id'],\n 'start_line_no': start_line_no,\n 'end_line_no': end_line_no,\n 'window_size': self.window_size,\n 'context_start_lineno': context_start_lineno,\n 'repo': self.repo\n }\n })\n print(f'build {len(code_windows)} prediction windows for {self.repo} with window size {self.window_size}')\n output_path = self.window_path_builder(self.prediction_path, self.repo, self.window_size)\n Tools.dump_pickle(code_windows, output_path)\n\nclass MakeWindowWrapper:\n def __init__(self, benchmark, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n\n self.benchmark = benchmark\n\n if benchmark == CONSTANTS.line_benchmark:\n self.task_file_path = FilePathBuilder.random_line_completion_benchmark\n elif benchmark == CONSTANTS.api_benchmark:\n self.task_file_path = FilePathBuilder.api_completion_benchmark\n elif benchmark == CONSTANTS.short_line_benchmark:\n self.task_file_path = FilePathBuilder.short_random_line_completion_benchmark\n elif benchmark == CONSTANTS.short_api_benchmark:\n self.task_file_path = FilePathBuilder.short_api_completion_benchmark\n\n def window_for_repo_files(self):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n for repo in self.repos:\n repo_window_maker = RepoWindowMaker(repo, window_size, slice_size)\n repo_window_maker.build_windows()\n\n def window_for_baseline_and_ground(self):\n tasks = Tools.load_jsonl(self.task_file_path)\n for window_size in self.window_sizes:\n for repo in self.repos:\n baseline_window_maker = BaselineWindowMaker(self.benchmark, repo, window_size, tasks)\n ground_window_maker = GroundTruthWindowMaker(self.benchmark, repo, window_size, tasks)\n baseline_window_maker.build_window()\n ground_window_maker.build_window()\n\n def window_for_prediction(self, mode, prediction_path_template):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n prediction_path = prediction_path_template.format(window_size=window_size, slice_size=slice_size)\n for repo in self.repos:\n window_path_builder = functools.partial(FilePathBuilder.gen_first_window_path, self.benchmark, mode)\n pred_window_maker = PredictionWindowMaker(repo, window_size, prediction_path, window_path_builder)\n pred_window_maker.build_window()","source_hash":"45b01cfe027a887c0f3da521076a384b1577a1189ff7940842d706bb90c47603","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.make_window.__init__","uri":"program://CodeT/function/RepoCoder.make_window.__init__#L192-L206","kind":"function","name":"__init__","path":"RepoCoder/make_window.py","language":"python","start_line":192,"end_line":206,"context_start_line":172,"context_end_line":226,"code":" continue\n code_windows.append({\n 'context': '\\n'.join(window_lines),\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no, # line_no starts from 0\n 'prediction': sample,\n 'task_id': prediction['metadata']['task_id'],\n 'start_line_no': start_line_no,\n 'end_line_no': end_line_no,\n 'window_size': self.window_size,\n 'context_start_lineno': context_start_lineno,\n 'repo': self.repo\n }\n })\n print(f'build {len(code_windows)} prediction windows for {self.repo} with window size {self.window_size}')\n output_path = self.window_path_builder(self.prediction_path, self.repo, self.window_size)\n Tools.dump_pickle(code_windows, output_path)\n\nclass MakeWindowWrapper:\n def __init__(self, benchmark, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n\n self.benchmark = benchmark\n\n if benchmark == CONSTANTS.line_benchmark:\n self.task_file_path = FilePathBuilder.random_line_completion_benchmark\n elif benchmark == CONSTANTS.api_benchmark:\n self.task_file_path = FilePathBuilder.api_completion_benchmark\n elif benchmark == CONSTANTS.short_line_benchmark:\n self.task_file_path = FilePathBuilder.short_random_line_completion_benchmark\n elif benchmark == CONSTANTS.short_api_benchmark:\n self.task_file_path = FilePathBuilder.short_api_completion_benchmark\n\n def window_for_repo_files(self):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n for repo in self.repos:\n repo_window_maker = RepoWindowMaker(repo, window_size, slice_size)\n repo_window_maker.build_windows()\n\n def window_for_baseline_and_ground(self):\n tasks = Tools.load_jsonl(self.task_file_path)\n for window_size in self.window_sizes:\n for repo in self.repos:\n baseline_window_maker = BaselineWindowMaker(self.benchmark, repo, window_size, tasks)\n ground_window_maker = GroundTruthWindowMaker(self.benchmark, repo, window_size, tasks)\n baseline_window_maker.build_window()\n ground_window_maker.build_window()\n\n def window_for_prediction(self, mode, prediction_path_template):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n prediction_path = prediction_path_template.format(window_size=window_size, slice_size=slice_size)\n for repo in self.repos:","source_hash":"45b01cfe027a887c0f3da521076a384b1577a1189ff7940842d706bb90c47603","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.make_window._buid_windows_for_a_file","uri":"program://CodeT/function/RepoCoder.make_window._buid_windows_for_a_file#L18-L41","kind":"function","name":"_buid_windows_for_a_file","path":"RepoCoder/make_window.py","language":"python","start_line":18,"end_line":41,"context_start_line":1,"context_end_line":61,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport itertools\nimport functools\n\nfrom utils import Tools, FilePathBuilder, CONSTANTS\nfrom collections import defaultdict\n\nclass RepoWindowMaker:\n def __init__(self, repo, window_size, slice_size):\n self.repo = repo\n self.window_size = window_size\n self.slice_size = slice_size\n self.slice_step = 1 if window_size // slice_size == 0 else window_size // slice_size\n self.source_code_files = Tools.iterate_repository(repo)\n \n def _buid_windows_for_a_file(self, fpath_tuple, code):\n code_windows = []\n code_lines = code.splitlines()\n delta_size = self.window_size // 2\n for line_no in range(0, len(code_lines), self.slice_step): # line_no starts from 0\n start_line_no = max(0, line_no - delta_size)\n end_line_no = min(len(code_lines), line_no + self.window_size - delta_size)\n window_lines = [i for i in code_lines[start_line_no:end_line_no]]\n if not window_lines: # all empty lines\n continue\n window_text = '\\n'.join(window_lines)\n code_windows.append({\n 'context': window_text,\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no,\n 'start_line_no': start_line_no,\n 'end_line_no': end_line_no,\n 'window_size': self.window_size,\n 'repo': self.repo,\n 'slice_size': self.slice_size,\n }\n })\n return code_windows\n \n def _merge_windows_with_same_context(self, code_windows):\n merged_code_windows = defaultdict(list)\n for code_window in code_windows:\n context = code_window['context']\n metadata = code_window['metadata']\n merged_code_windows[context].append(metadata)\n json_lines = []\n for context, metadata_list in merged_code_windows.items():\n json_lines.append({\n 'context': context,\n 'metadata': metadata_list\n })\n return json_lines\n\n def build_windows(self):\n all_code_windows = []\n for fpath_tuple, code in self.source_code_files.items():\n all_code_windows += self._buid_windows_for_a_file(fpath_tuple, code)\n merged_code_windows = self._merge_windows_with_same_context(all_code_windows)","source_hash":"45b01cfe027a887c0f3da521076a384b1577a1189ff7940842d706bb90c47603","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.make_window._merge_windows_with_same_context","uri":"program://CodeT/function/RepoCoder.make_window._merge_windows_with_same_context#L43-L55","kind":"function","name":"_merge_windows_with_same_context","path":"RepoCoder/make_window.py","language":"python","start_line":43,"end_line":55,"context_start_line":23,"context_end_line":75,"code":" start_line_no = max(0, line_no - delta_size)\n end_line_no = min(len(code_lines), line_no + self.window_size - delta_size)\n window_lines = [i for i in code_lines[start_line_no:end_line_no]]\n if not window_lines: # all empty lines\n continue\n window_text = '\\n'.join(window_lines)\n code_windows.append({\n 'context': window_text,\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no,\n 'start_line_no': start_line_no,\n 'end_line_no': end_line_no,\n 'window_size': self.window_size,\n 'repo': self.repo,\n 'slice_size': self.slice_size,\n }\n })\n return code_windows\n \n def _merge_windows_with_same_context(self, code_windows):\n merged_code_windows = defaultdict(list)\n for code_window in code_windows:\n context = code_window['context']\n metadata = code_window['metadata']\n merged_code_windows[context].append(metadata)\n json_lines = []\n for context, metadata_list in merged_code_windows.items():\n json_lines.append({\n 'context': context,\n 'metadata': metadata_list\n })\n return json_lines\n\n def build_windows(self):\n all_code_windows = []\n for fpath_tuple, code in self.source_code_files.items():\n all_code_windows += self._buid_windows_for_a_file(fpath_tuple, code)\n merged_code_windows = self._merge_windows_with_same_context(all_code_windows)\n print(f'build {len(merged_code_windows)} windows for {self.repo} with window size {self.window_size} and slice {self.slice_size}')\n output_path = FilePathBuilder.repo_windows_path(self.repo, self.window_size, self.slice_size)\n Tools.dump_pickle(merged_code_windows, output_path)\n\n\nclass BaselineWindowMaker:\n '''the retrieve-and-generate approach'''\n def __init__(self, benchmark, repo, window_size, tasks):\n self.benchmark = benchmark\n self.repo = repo\n self.window_size = window_size\n self.tasks = tasks\n self.source_code = Tools.iterate_repository(repo)\n ","source_hash":"45b01cfe027a887c0f3da521076a384b1577a1189ff7940842d706bb90c47603","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.make_window.build_windows","uri":"program://CodeT/function/RepoCoder.make_window.build_windows#L57-L64","kind":"function","name":"build_windows","path":"RepoCoder/make_window.py","language":"python","start_line":57,"end_line":64,"context_start_line":37,"context_end_line":84,"code":" 'repo': self.repo,\n 'slice_size': self.slice_size,\n }\n })\n return code_windows\n \n def _merge_windows_with_same_context(self, code_windows):\n merged_code_windows = defaultdict(list)\n for code_window in code_windows:\n context = code_window['context']\n metadata = code_window['metadata']\n merged_code_windows[context].append(metadata)\n json_lines = []\n for context, metadata_list in merged_code_windows.items():\n json_lines.append({\n 'context': context,\n 'metadata': metadata_list\n })\n return json_lines\n\n def build_windows(self):\n all_code_windows = []\n for fpath_tuple, code in self.source_code_files.items():\n all_code_windows += self._buid_windows_for_a_file(fpath_tuple, code)\n merged_code_windows = self._merge_windows_with_same_context(all_code_windows)\n print(f'build {len(merged_code_windows)} windows for {self.repo} with window size {self.window_size} and slice {self.slice_size}')\n output_path = FilePathBuilder.repo_windows_path(self.repo, self.window_size, self.slice_size)\n Tools.dump_pickle(merged_code_windows, output_path)\n\n\nclass BaselineWindowMaker:\n '''the retrieve-and-generate approach'''\n def __init__(self, benchmark, repo, window_size, tasks):\n self.benchmark = benchmark\n self.repo = repo\n self.window_size = window_size\n self.tasks = tasks\n self.source_code = Tools.iterate_repository(repo)\n \n def build_window(self):\n code_windows = []\n for task in self.tasks:\n if task['metadata']['task_id'].split('/')[0] != self.repo:\n continue\n fpath_tuple = tuple(task['metadata']['fpath_tuple'])\n line_no = task['metadata']['line_no']\n original_code = self.source_code[fpath_tuple]\n code_lines = original_code.splitlines()","source_hash":"45b01cfe027a887c0f3da521076a384b1577a1189ff7940842d706bb90c47603","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.make_window.build_window","uri":"program://CodeT/function/RepoCoder.make_window.build_window#L153-L189","kind":"function","name":"build_window","path":"RepoCoder/make_window.py","language":"python","start_line":153,"end_line":189,"context_start_line":133,"context_end_line":209,"code":" 'start_line_no': start_line_no,\n 'end_line_no': end_line_no,\n 'window_size': self.window_size,\n 'context_start_lineno': context_start_lineno,\n 'repo': self.repo\n }\n })\n print(f'build {len(code_windows)} ground truth windows for {self.repo} with window size {self.window_size}')\n output_path = FilePathBuilder.search_first_window_path(self.benchmark, CONSTANTS.rg, self.repo, self.window_size)\n Tools.dump_pickle(code_windows, output_path)\n\nclass PredictionWindowMaker:\n def __init__(self, repo, window_size, prediction_path, window_path_builder):\n self.repo = repo\n self.window_size = window_size\n self.prediction_path = prediction_path\n self.source_code = Tools.iterate_repository(repo)\n self.predictions = Tools.load_jsonl(prediction_path)\n self.window_path_builder = window_path_builder\n \n def build_window(self, type='centered'):\n code_windows = []\n delta_size = self.window_size // 2\n for prediction in self.predictions:\n if prediction['metadata']['task_id'].split('/')[0] != self.repo:\n continue\n fpath_tuple = tuple(prediction['metadata']['fpath_tuple'])\n line_no = prediction['metadata']['line_no'] # line_no in prediction file starts from 0\n original_code = self.source_code[fpath_tuple]\n code_lines = original_code.splitlines()\n context_start_lineno = prediction['metadata']['context_start_lineno']\n start_line_no = max(context_start_lineno, line_no - delta_size)\n for sample in [prediction['choices'][i]['text'] for i in range(len(prediction['choices']))]:\n # TODO actually only one sample is generated\n sample_lines = [i for i in sample.splitlines() if i.strip()]\n new_code_lines = code_lines[:line_no] + sample_lines\n end_line_no = min(len(new_code_lines), line_no + self.window_size - delta_size)\n window_lines = [i for i in new_code_lines[start_line_no:end_line_no] if i.strip()]\n if not window_lines: # all empty lines\n continue\n code_windows.append({\n 'context': '\\n'.join(window_lines),\n 'metadata': {\n 'fpath_tuple': fpath_tuple,\n 'line_no': line_no, # line_no starts from 0\n 'prediction': sample,\n 'task_id': prediction['metadata']['task_id'],\n 'start_line_no': start_line_no,\n 'end_line_no': end_line_no,\n 'window_size': self.window_size,\n 'context_start_lineno': context_start_lineno,\n 'repo': self.repo\n }\n })\n print(f'build {len(code_windows)} prediction windows for {self.repo} with window size {self.window_size}')\n output_path = self.window_path_builder(self.prediction_path, self.repo, self.window_size)\n Tools.dump_pickle(code_windows, output_path)\n\nclass MakeWindowWrapper:\n def __init__(self, benchmark, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n\n self.benchmark = benchmark\n\n if benchmark == CONSTANTS.line_benchmark:\n self.task_file_path = FilePathBuilder.random_line_completion_benchmark\n elif benchmark == CONSTANTS.api_benchmark:\n self.task_file_path = FilePathBuilder.api_completion_benchmark\n elif benchmark == CONSTANTS.short_line_benchmark:\n self.task_file_path = FilePathBuilder.short_random_line_completion_benchmark\n elif benchmark == CONSTANTS.short_api_benchmark:\n self.task_file_path = FilePathBuilder.short_api_completion_benchmark\n\n def window_for_repo_files(self):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):","source_hash":"45b01cfe027a887c0f3da521076a384b1577a1189ff7940842d706bb90c47603","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.make_window.window_for_repo_files","uri":"program://CodeT/function/RepoCoder.make_window.window_for_repo_files#L208-L212","kind":"function","name":"window_for_repo_files","path":"RepoCoder/make_window.py","language":"python","start_line":208,"end_line":212,"context_start_line":188,"context_end_line":229,"code":" output_path = self.window_path_builder(self.prediction_path, self.repo, self.window_size)\n Tools.dump_pickle(code_windows, output_path)\n\nclass MakeWindowWrapper:\n def __init__(self, benchmark, repos, window_sizes, slice_sizes):\n self.repos = repos\n self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n\n self.benchmark = benchmark\n\n if benchmark == CONSTANTS.line_benchmark:\n self.task_file_path = FilePathBuilder.random_line_completion_benchmark\n elif benchmark == CONSTANTS.api_benchmark:\n self.task_file_path = FilePathBuilder.api_completion_benchmark\n elif benchmark == CONSTANTS.short_line_benchmark:\n self.task_file_path = FilePathBuilder.short_random_line_completion_benchmark\n elif benchmark == CONSTANTS.short_api_benchmark:\n self.task_file_path = FilePathBuilder.short_api_completion_benchmark\n\n def window_for_repo_files(self):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n for repo in self.repos:\n repo_window_maker = RepoWindowMaker(repo, window_size, slice_size)\n repo_window_maker.build_windows()\n\n def window_for_baseline_and_ground(self):\n tasks = Tools.load_jsonl(self.task_file_path)\n for window_size in self.window_sizes:\n for repo in self.repos:\n baseline_window_maker = BaselineWindowMaker(self.benchmark, repo, window_size, tasks)\n ground_window_maker = GroundTruthWindowMaker(self.benchmark, repo, window_size, tasks)\n baseline_window_maker.build_window()\n ground_window_maker.build_window()\n\n def window_for_prediction(self, mode, prediction_path_template):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n prediction_path = prediction_path_template.format(window_size=window_size, slice_size=slice_size)\n for repo in self.repos:\n window_path_builder = functools.partial(FilePathBuilder.gen_first_window_path, self.benchmark, mode)\n pred_window_maker = PredictionWindowMaker(repo, window_size, prediction_path, window_path_builder)\n pred_window_maker.build_window()","source_hash":"45b01cfe027a887c0f3da521076a384b1577a1189ff7940842d706bb90c47603","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.make_window.window_for_baseline_and_ground","uri":"program://CodeT/function/RepoCoder.make_window.window_for_baseline_and_ground#L214-L221","kind":"function","name":"window_for_baseline_and_ground","path":"RepoCoder/make_window.py","language":"python","start_line":214,"end_line":221,"context_start_line":194,"context_end_line":229,"code":" self.window_sizes = window_sizes\n self.slice_sizes = slice_sizes\n\n self.benchmark = benchmark\n\n if benchmark == CONSTANTS.line_benchmark:\n self.task_file_path = FilePathBuilder.random_line_completion_benchmark\n elif benchmark == CONSTANTS.api_benchmark:\n self.task_file_path = FilePathBuilder.api_completion_benchmark\n elif benchmark == CONSTANTS.short_line_benchmark:\n self.task_file_path = FilePathBuilder.short_random_line_completion_benchmark\n elif benchmark == CONSTANTS.short_api_benchmark:\n self.task_file_path = FilePathBuilder.short_api_completion_benchmark\n\n def window_for_repo_files(self):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n for repo in self.repos:\n repo_window_maker = RepoWindowMaker(repo, window_size, slice_size)\n repo_window_maker.build_windows()\n\n def window_for_baseline_and_ground(self):\n tasks = Tools.load_jsonl(self.task_file_path)\n for window_size in self.window_sizes:\n for repo in self.repos:\n baseline_window_maker = BaselineWindowMaker(self.benchmark, repo, window_size, tasks)\n ground_window_maker = GroundTruthWindowMaker(self.benchmark, repo, window_size, tasks)\n baseline_window_maker.build_window()\n ground_window_maker.build_window()\n\n def window_for_prediction(self, mode, prediction_path_template):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n prediction_path = prediction_path_template.format(window_size=window_size, slice_size=slice_size)\n for repo in self.repos:\n window_path_builder = functools.partial(FilePathBuilder.gen_first_window_path, self.benchmark, mode)\n pred_window_maker = PredictionWindowMaker(repo, window_size, prediction_path, window_path_builder)\n pred_window_maker.build_window()","source_hash":"45b01cfe027a887c0f3da521076a384b1577a1189ff7940842d706bb90c47603","truncated":false} {"repo_id":"CodeT","entity_id":"py:RepoCoder.make_window.window_for_prediction","uri":"program://CodeT/function/RepoCoder.make_window.window_for_prediction#L223-L229","kind":"function","name":"window_for_prediction","path":"RepoCoder/make_window.py","language":"python","start_line":223,"end_line":229,"context_start_line":203,"context_end_line":229,"code":" elif benchmark == CONSTANTS.short_line_benchmark:\n self.task_file_path = FilePathBuilder.short_random_line_completion_benchmark\n elif benchmark == CONSTANTS.short_api_benchmark:\n self.task_file_path = FilePathBuilder.short_api_completion_benchmark\n\n def window_for_repo_files(self):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n for repo in self.repos:\n repo_window_maker = RepoWindowMaker(repo, window_size, slice_size)\n repo_window_maker.build_windows()\n\n def window_for_baseline_and_ground(self):\n tasks = Tools.load_jsonl(self.task_file_path)\n for window_size in self.window_sizes:\n for repo in self.repos:\n baseline_window_maker = BaselineWindowMaker(self.benchmark, repo, window_size, tasks)\n ground_window_maker = GroundTruthWindowMaker(self.benchmark, repo, window_size, tasks)\n baseline_window_maker.build_window()\n ground_window_maker.build_window()\n\n def window_for_prediction(self, mode, prediction_path_template):\n for window_size, slice_size in itertools.product(self.window_sizes, self.slice_sizes):\n prediction_path = prediction_path_template.format(window_size=window_size, slice_size=slice_size)\n for repo in self.repos:\n window_path_builder = functools.partial(FilePathBuilder.gen_first_window_path, self.benchmark, mode)\n pred_window_maker = PredictionWindowMaker(repo, window_size, prediction_path, window_path_builder)\n pred_window_maker.build_window()","source_hash":"45b01cfe027a887c0f3da521076a384b1577a1189ff7940842d706bb90c47603","truncated":false} {"repo_id":"CodeT","entity_id":"file:DIVERSE/code/src/utils_io.py","uri":"program://CodeT/file/DIVERSE/code/src/utils_io.py","kind":"file","name":"DIVERSE/code/src/utils_io.py","path":"DIVERSE/code/src/utils_io.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":7,"code":"import os\n\ndef get_file(path):\n if os.path.isdir(path):\n return os.path.join(path, os.listdir(path)[0])\n else:\n return path","source_hash":"8f763f060cbc05ee517cdd35fbd7b2f0c8e34d2dd0149904aba9864a028e5146","truncated":false} {"repo_id":"CodeT","entity_id":"file:DIVERSE/code/src/deberta_model.py","uri":"program://CodeT/file/DIVERSE/code/src/deberta_model.py","kind":"file","name":"DIVERSE/code/src/deberta_model.py","path":"DIVERSE/code/src/deberta_model.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# coding=utf-8\n# Copyright 2020 Microsoft and the Hugging Face Inc. team.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" PyTorch DeBERTa-v2 model. \"\"\"\nimport pdb\nimport math\nfrom collections.abc import Sequence\n\nimport numpy as np\nimport torch","source_hash":"55c54fdb9ca67ec81ec8ac279b59946b703512d073d57232e1932beab7a484be","truncated":false} {"repo_id":"CodeT","entity_id":"file:DIVERSE/code/src/run_ner.py","uri":"program://CodeT/file/DIVERSE/code/src/run_ner.py","kind":"file","name":"DIVERSE/code/src/run_ner.py","path":"DIVERSE/code/src/run_ner.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" Fine-tuning the library models for named entity recognition on CoNLL-2003. \"\"\"\nimport sys\nimport logging\nimport os\nfrom dataclasses import dataclass, field\nfrom importlib import import_module","source_hash":"4b7c42efb152bbd5ec90f25edf0119a789e074460cc95761b1d4f6c7b78c0e04","truncated":false} {"repo_id":"CodeT","entity_id":"file:DIVERSE/code/src/verifier_metrics.py","uri":"program://CodeT/file/DIVERSE/code/src/verifier_metrics.py","kind":"file","name":"DIVERSE/code/src/verifier_metrics.py","path":"DIVERSE/code/src/verifier_metrics.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import absl # Here to have a nice missing dependency error message early on\nimport nltk # Here to have a nice missing dependency error message early on\nimport numpy # Here to have a nice missing dependency error message early on\nimport six # Here to have a nice missing dependency error message early on\nfrom rouge_score import rouge_scorer, scoring\nimport datasets\nimport pdb\nimport numpy as np\nimport scipy\nfrom tqdm import tqdm\n\nfrom utils import (\n GSM8KCase,\n GSM8KExample,\n TextEntailmentCase,\n TextEntailmentExample,\n convert_eval_sequences_to_cases,\n compute_results,\n compute_results_avg,\n)\n","source_hash":"e167936509daa986498fce3a20defb189642acfa5c6e1469e18c6b009399a459","truncated":false} {"repo_id":"CodeT","entity_id":"file:DIVERSE/code/src/verifier_data_prepare.py","uri":"program://CodeT/file/DIVERSE/code/src/verifier_data_prepare.py","kind":"file","name":"DIVERSE/code/src/verifier_data_prepare.py","path":"DIVERSE/code/src/verifier_data_prepare.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import os\nimport json\nimport random\nimport argparse\nfrom tqdm import tqdm\nimport re\nimport utils_io\nfrom utils import (\n GSM8KCase,\n TextEntailmentCase,\n GSM8KExample,\n TextEntailmentExample,\n compute_top1_and_recall,\n post_process_answer_clutrr_mapping,\n post_process_answer_clutrr_cutoff,\n)\nfrom transformers import (\n AutoTokenizer,\n AutoModelForSequenceClassification,\n)\nimport torch","source_hash":"955b9f68d4841759bf1c3467ad43446e738d69fb4db84eec80d086ec766cc7c3","truncated":false} {"repo_id":"CodeT","entity_id":"file:DIVERSE/code/src/utils.py","uri":"program://CodeT/file/DIVERSE/code/src/utils.py","kind":"file","name":"DIVERSE/code/src/utils.py","path":"DIVERSE/code/src/utils.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import re\nfrom tqdm import tqdm\nfrom multiset import Multiset\nfrom functools import lru_cache\nimport random\nimport json\nimport pdb\nimport torch\nimport torch.nn.functional as F\nimport numpy as np\nfrom transformers import (\n AutoModelForSequenceClassification,\n AutoTokenizer,\n pipeline,\n)\nimport time\n\n\nclass BaseCase:\n def __init__(self, ground_truth, preds):\n self.question = \"\"","source_hash":"e01ce8b38fc7fa049901b5bd06dc86df116285d01736c457f2eb7e6c56fa5870","truncated":false} {"repo_id":"CodeT","entity_id":"file:DIVERSE/code/src/utils_ner.py","uri":"program://CodeT/file/DIVERSE/code/src/utils_ner.py","kind":"file","name":"DIVERSE/code/src/utils_ner.py","path":"DIVERSE/code/src/utils_ner.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# coding=utf-8\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\" Named entity recognition fine-tuning: utilities to work with CoNLL-2003 task. \"\"\"\n\n\nimport logging\nimport os\nfrom dataclasses import dataclass","source_hash":"ee8fd32fbf0eb81dca61ced476578074741dd9f0c18b5b51c92d3d0ef0fc2b1e","truncated":false} {"repo_id":"CodeT","entity_id":"file:DIVERSE/code/src/tasks.py","uri":"program://CodeT/file/DIVERSE/code/src/tasks.py","kind":"file","name":"DIVERSE/code/src/tasks.py","path":"DIVERSE/code/src/tasks.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"import logging\nimport os\nfrom typing import List, TextIO, Union\n\nfrom conllu import parse_incr\n\nfrom utils_ner import InputExample, Split, TokenClassificationTask\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass NER(TokenClassificationTask):\n def __init__(self, label_idx=-1):\n # in NER datasets, the last column is usually reserved for NER label\n self.label_idx = label_idx\n\n def read_examples_from_file(self, data_dir, mode: Union[Split, str]) -> List[InputExample]:\n if isinstance(mode, Split):\n mode = mode.value\n file_path = os.path.join(data_dir, f\"{mode}.txt\")","source_hash":"6c47ec218bcb6c0bc4fe1fc726384a91ab255e0f750aa4e0b1ee2dee7c549579","truncated":false} {"repo_id":"CodeT","entity_id":"file:CodeT/main.py","uri":"program://CodeT/file/CodeT/main.py","kind":"file","name":"CodeT/main.py","path":"CodeT/main.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport argparse\nimport logging\nimport os\n\nfrom src.postprocess import PostProcessor\nfrom src.execution import evaluate_with_test_code, evaluate_with_test_cases\nfrom src.io_utils import Tools\nfrom src.agreement import DataManager, DualAgreement\nfrom src.evaluation import pass_at_K, get_result_of_sorted_solutions\n\nlogging.basicConfig(\n format=\"SystemLog: [%(asctime)s][%(name)s][%(levelname)s] - %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n level=logging.INFO,\n)\n\nlogger = logging.getLogger(__name__)\n","source_hash":"b635824dc6f4d0e6dc4ce3d913d5f769699efe9186b24ee37c5454e20efa1013","truncated":false} {"repo_id":"CodeT","entity_id":"file:CodeT/src/io_utils.py","uri":"program://CodeT/file/CodeT/src/io_utils.py","kind":"file","name":"CodeT/src/io_utils.py","path":"CodeT/src/io_utils.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport json\nimport pickle\n\nclass Tools:\n @staticmethod\n def load_jsonl(file_path):\n json_objects = []\n with open(file_path, 'r', encoding='utf8') as f:\n for line in f:\n json_objects.append(json.loads(line.strip()))\n return json_objects\n \n @staticmethod\n def load_tasks(task_path):\n result = dict()\n lines = Tools.load_jsonl(task_path)\n for line in lines:\n result[line['task_id']] = line","source_hash":"0c5eb6d103a379d4a7c961e4948df2ad3b612ff0f8e52c48cea0a8f2964eff2a","truncated":false} {"repo_id":"CodeT","entity_id":"file:CodeT/src/agreement.py","uri":"program://CodeT/file/CodeT/src/agreement.py","kind":"file","name":"CodeT/src/agreement.py","path":"CodeT/src/agreement.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom collections import defaultdict, Counter\nimport logging\nimport math\n\n\nlogging.basicConfig(\n format=\"SystemLog: [%(asctime)s][%(name)s][%(levelname)s] - %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n level=logging.INFO,\n)\n\nlogger = logging.getLogger(__name__)\n\nclass DataManager:\n def __init__(self, dual_exec_results, sampled_code_by_task, sampled_test_case_by_task, limit):\n logger.info('handling dual exec results')\n self.dual_exec_results = dual_exec_results\n self.sampled_code_by_task = sampled_code_by_task","source_hash":"5c48e564cec6cb30ad3880cda742af2bbe2b35f4e2581e22014dea0922765ef5","truncated":false} {"repo_id":"CodeT","entity_id":"file:CodeT/src/postprocess.py","uri":"program://CodeT/file/CodeT/src/postprocess.py","kind":"file","name":"CodeT/src/postprocess.py","path":"CodeT/src/postprocess.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom collections import defaultdict\n\nfrom src.io_utils import Tools\n\nSTOP_TOKEN = ['\\nclass', '\\ndef', '\\n#', '\\nif', '\\nprint']\n\nclass PostProcessor:\n @staticmethod\n def map_task_id_for_solution(predict_path, source_path):\n database = dict()\n raw_problems = Tools.load_tasks(source_path)\n for task_id in raw_problems.keys():\n database[raw_problems[task_id]['prompt']] = raw_problems[task_id]\n\n result = []\n predictions = Tools.load_jsonl(predict_path)\n for pre in predictions:\n task = database[pre['prompt']]","source_hash":"7ec392f825cb31702d88827a8fecba44178e785a13780d5f76db3332b1bd0331","truncated":false} {"repo_id":"CodeT","entity_id":"file:CodeT/src/evaluation.py","uri":"program://CodeT/file/CodeT/src/evaluation.py","kind":"file","name":"CodeT/src/evaluation.py","path":"CodeT/src/evaluation.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport statistics\nimport numpy as np\nfrom collections import defaultdict\nimport logging\nfrom typing import List, Union\nimport itertools\n\nlogging.basicConfig(\n format=\"SystemLog: [%(asctime)s][%(name)s][%(levelname)s] - %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n level=logging.INFO,\n)\n\nlogger = logging.getLogger(__name__)\n\ndef _dictionized_ground_truth_results(ground_truth_exec_results):\n ground_truth_results_by_task_and_solution = defaultdict(defaultdict)\n for result in ground_truth_exec_results:","source_hash":"25ce065e7452ea0a489d57a0ce8c049edcd383f98c21819462d05e222fe840c0","truncated":false} {"repo_id":"CodeT","entity_id":"file:CodeT/src/_execution.py","uri":"program://CodeT/file/CodeT/src/_execution.py","kind":"file","name":"CodeT/src/_execution.py","path":"CodeT/src/_execution.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom typing import Optional, Dict\nimport contextlib\nimport faulthandler\nimport io\nimport os\nimport multiprocessing\nimport platform\nimport signal\nimport tempfile\n\ndef _pack_test_cases(test_cases, timeout):\n blank_4 = ' ' * 4\n blank_8 = ' ' * 8\n blank_12 = ' ' * 12\n result = f'def check():\\n pass_result = []\\n'\n for idx, tc in enumerate(test_cases):\n multi_line_assertion = tc.strip().replace('\\n', f'\\n{blank_12}')\n result += f'\\n{blank_4}try:\\n{blank_8}with time_limit({timeout}):\\n{blank_12}{multi_line_assertion}\\","source_hash":"881d0e9c75c8e8308f818487f473544d1775a6c4f4fc1fc3a03fe3963cdcad29","truncated":false} {"repo_id":"CodeT","entity_id":"file:CodeT/src/execution.py","uri":"program://CodeT/file/CodeT/src/execution.py","kind":"file","name":"CodeT/src/execution.py","path":"CodeT/src/execution.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport ctypes\nlibgcc_s = ctypes.CDLL('libgcc_s.so.1')\n\nfrom collections import defaultdict\nfrom concurrent.futures import as_completed, ProcessPoolExecutor\nimport logging\n\nfrom src._execution import check_correctness, check_correctness_with_test_cases\n\nlogging.basicConfig(\n format=\"SystemLog: [%(asctime)s][%(name)s][%(levelname)s] - %(message)s\",\n datefmt=\"%Y-%m-%d %H:%M:%S\",\n level=logging.INFO,\n)\n\nlogger = logging.getLogger(__name__)\n\ndef evaluate_with_test_code(","source_hash":"7743c9a58e55de4bd4ca551e91417d80610fa686c0dfae5627624957d6868170","truncated":false} {"repo_id":"CodeT","entity_id":"file:RepoCoder/build_vector.py","uri":"program://CodeT/file/RepoCoder/build_vector.py","kind":"file","name":"RepoCoder/build_vector.py","path":"RepoCoder/build_vector.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport tqdm\nimport itertools\nfrom collections import defaultdict\nfrom concurrent.futures import as_completed, ProcessPoolExecutor\n\nfrom utils import Tools, FilePathBuilder, CONSTANTS\n\nclass BagOfWords:\n def __init__(self, input_file):\n self.input_file = input_file\n\n def build(self):\n print(f'building one gram vector for {self.input_file}')\n futures = dict()\n lines = Tools.load_pickle(self.input_file)\n with ProcessPoolExecutor(max_workers=48) as executor:\n for line in lines:\n futures[executor.submit(Tools.tokenize, line['context'])] = line","source_hash":"624930fae7b34d417eb8f8c1668788682cf0958c60ed4b7938dcdffdf7bfe18e","truncated":false} {"repo_id":"CodeT","entity_id":"file:RepoCoder/search_code.py","uri":"program://CodeT/file/RepoCoder/search_code.py","kind":"file","name":"RepoCoder/search_code.py","path":"RepoCoder/search_code.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nfrom concurrent.futures import as_completed, ProcessPoolExecutor\nimport numpy as np\nimport scipy\nimport tqdm\nimport os\nimport copy\nimport functools\n\nfrom utils import Tools, FilePathBuilder, CONSTANTS\n\nclass SimilarityScore:\n @staticmethod\n def cosine_similarity(embedding_vec1, embedding_vec2):\n return 1 - scipy.spatial.distance.cosine(embedding_vec1, embedding_vec2)\n \n @staticmethod\n def jaccard_similarity(list1, list2):\n set1 = set(list1)","source_hash":"977bc3469390b030dc2f47cd8a9b4449c9142308e58afe2b90d0fecad7359ba3","truncated":false} {"repo_id":"CodeT","entity_id":"file:RepoCoder/run_pipeline.py","uri":"program://CodeT/file/RepoCoder/run_pipeline.py","kind":"file","name":"RepoCoder/run_pipeline.py","path":"RepoCoder/run_pipeline.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport os\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n\nfrom make_window import MakeWindowWrapper\nfrom build_vector import BuildVectorWrapper, BagOfWords\nfrom search_code import CodeSearchWrapper\nfrom build_prompt import BuildPromptWrapper\n\nfrom utils import CONSTANTS, CodexTokenizer\n\ndef make_repo_window(repos, window_sizes, slice_sizes):\n worker = MakeWindowWrapper(None, repos, window_sizes, slice_sizes)\n worker.window_for_repo_files()\n\n\ndef run_RG1_and_oracle_method(benchmark, repos, window_sizes, slice_sizes):\n # build code snippets for all the repositories\n make_repo_window(repos, window_sizes, slice_sizes)","source_hash":"b08804b7d4de453ee1d0cdc8333c0658608df3b67a4a298bf447421f08da7bf6","truncated":false} {"repo_id":"CodeT","entity_id":"file:RepoCoder/utils.py","uri":"program://CodeT/file/RepoCoder/utils.py","kind":"file","name":"RepoCoder/utils.py","path":"RepoCoder/utils.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport os\nimport glob\nimport pickle\nimport json\nimport tiktoken\nfrom transformers import AutoTokenizer\n\nclass CONSTANTS:\n # regular version for Codex\n api_benchmark = 'random_api'\n line_benchmark = 'random_line'\n # short version for CodeGen\n short_api_benchmark = 'short_api'\n short_line_benchmark = 'short_line'\n gt = 'gt'\n rg = 'r-g' # vanilla retrieval-augmented approach\n rgrg = 'r-g-r-g' # RepoCoder, two-stage retrieval and generation\n","source_hash":"f841c60c1ecc223db5e72986f7e8e2ff0379c4bda5902bcba9adc02cba045fea","truncated":false} {"repo_id":"CodeT","entity_id":"file:RepoCoder/compute_score.py","uri":"program://CodeT/file/RepoCoder/compute_score.py","kind":"file","name":"RepoCoder/compute_score.py","path":"RepoCoder/compute_score.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport editdistance\nfrom collections import defaultdict\n\nfrom utils import Tools\n\ndef compute_EM(target, predictions, passk):\n target_lines = [line.strip() for line in target.splitlines() if line.strip()]\n EM_scores = []\n for prediction in predictions[:passk]:\n prediction_lines = [line.strip() for line in prediction.splitlines() if line.strip()][:len(target_lines)]\n if len(target_lines) != len(prediction_lines):\n EM_scores.append(0)\n continue\n if target_lines == prediction_lines:\n EM_scores.append(1)\n continue\n EM_scores.append(0)\n return any(EM_scores)","source_hash":"3a572b3b8f537d4e269ba10cd0accf62a3f517f754a1c1dd2a84630b880cca4c","truncated":false} {"repo_id":"CodeT","entity_id":"file:RepoCoder/build_prompt.py","uri":"program://CodeT/file/RepoCoder/build_prompt.py","kind":"file","name":"RepoCoder/build_prompt.py","path":"RepoCoder/build_prompt.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport functools\nimport os\n\nfrom utils import Tools, FilePathBuilder, CodexTokenizer, CodeGenTokenizer, CONSTANTS\n\nclass PromptBuilder:\n def __init__(self, query_lines_with_retrieval_results, task_path, log_message, tokenizer):\n self.query_lines_with_retrieval_results = query_lines_with_retrieval_results\n self.log_message = log_message\n if tokenizer == CodexTokenizer:\n self.tokenizer = CodexTokenizer()\n self.max_retrieval_length = 2000 # half of the max length of the model\n elif tokenizer == CodeGenTokenizer:\n self.tokenizer = CodeGenTokenizer()\n self.max_retrieval_length = 1000\n tasks = Tools.load_jsonl(task_path)\n self.tasks_by_task_id = {task['metadata']['task_id']: task for task in tasks}\n self.seperator = '# ' + '-' * 50","source_hash":"1907f0b26ba4fbd02b1b43cf88c0478b8ee9cad7e6fe15e5c769a8863c10039b","truncated":false} {"repo_id":"CodeT","entity_id":"file:RepoCoder/make_window.py","uri":"program://CodeT/file/RepoCoder/make_window.py","kind":"file","name":"RepoCoder/make_window.py","path":"RepoCoder/make_window.py","language":"python","start_line":1,"end_line":1,"context_start_line":1,"context_end_line":21,"code":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT license.\n\nimport itertools\nimport functools\n\nfrom utils import Tools, FilePathBuilder, CONSTANTS\nfrom collections import defaultdict\n\nclass RepoWindowMaker:\n def __init__(self, repo, window_size, slice_size):\n self.repo = repo\n self.window_size = window_size\n self.slice_size = slice_size\n self.slice_step = 1 if window_size // slice_size == 0 else window_size // slice_size\n self.source_code_files = Tools.iterate_repository(repo)\n \n def _buid_windows_for_a_file(self, fpath_tuple, code):\n code_windows = []\n code_lines = code.splitlines()\n delta_size = self.window_size // 2","source_hash":"45b01cfe027a887c0f3da521076a384b1577a1189ff7940842d706bb90c47603","truncated":false}