id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
2,159 | import copy
import math
import warnings
from typing import Optional, Tuple, Union
from loguru import logger
import torch
import torch.distributed
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseModelOutput,
Ba... | null |
2,160 | import math
import os
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.distributed
import torch.utils.checkpoint
from torch import nn
from torch.nn import LayerNorm
from torch.nn import functional as F
from transformers.modeling_outputs import (
BaseModelOutputWithPastAndCrossAtte... | Make causal mask used for self-attention. |
2,161 | import math
import os
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.distributed
import torch.utils.checkpoint
from torch import nn
from torch.nn import LayerNorm
from torch.nn import functional as F
from transformers.modeling_outputs import (
BaseModelOutputWithPastAndCrossAtte... | Expands attention_mask from `[batch_size, src_length]` to `[batch_size, 1, tgt_length, src_length]`. |
2,162 | import math
import os
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.distributed
import torch.utils.checkpoint
from torch import nn
from torch.nn import LayerNorm
from torch.nn import functional as F
from transformers.modeling_outputs import (
BaseModelOutputWithPastAndCrossAtte... | Link to paper: https://arxiv.org/abs/2108.12409 Alibi tensor is not causal as the original paper mentions, it relies on a translation invariance of softmax for quick implementation: with l being a tensor, and a fixed value `softmax(l+a) = softmax(l)`. Based on https://github.com/ofirpress/attention_with_linear_biases/b... |
2,163 | import math
import os
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.distributed
import torch.utils.checkpoint
from torch import nn
from torch.nn import LayerNorm
from torch.nn import functional as F
from transformers.modeling_outputs import (
BaseModelOutputWithPastAndCrossAtte... | Dropout add function Args: x (`torch.tensor`, *required*): input tensor residual (`torch.tensor`, *required*): esidual tensor prob (`float`, *required*): dropout probability training (`bool`, *required*): training mode |
2,164 | import math
import os
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.distributed
import torch.utils.checkpoint
from torch import nn
from torch.nn import LayerNorm
from torch.nn import functional as F
from transformers.modeling_outputs import (
BaseModelOutputWithPastAndCrossAtte... | Split the last dimension into (num_heads, head_dim) without making any copies, results share same memory storage as `fused_qkv` Args: fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim] Returns: query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num... |
2,165 | import math
import os
import warnings
from typing import Optional, Tuple, Union
import torch
import torch.distributed
import torch.utils.checkpoint
from torch import nn
from torch.nn import LayerNorm
from torch.nn import functional as F
from transformers.modeling_outputs import (
BaseModelOutputWithPastAndCrossAtte... | Merge heads together over the last dimenstion Args: x: (`torch.tensor`, *required*): [batch_size * num_heads, seq_length, head_dim] Returns: torch.tensor: [batch_size, seq_length, num_heads * head_dim] |
2,166 | import random
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
)
from transformers.modeling_utils import PreTrai... | Make causal mask used for bi-directional self-attention. |
2,167 | import random
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
)
from transformers.modeling_utils import PreTrai... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
2,168 | import math
import os
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
Cau... | null |
2,169 | import math
import os
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
Cau... | null |
2,170 | import math
import os
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
Cau... | null |
2,171 | import math
import os
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
Cau... | null |
2,172 | import math
import os
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
Cau... | null |
2,173 | import math
import os
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
Cau... | null |
2,174 | import math
import os
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
Cau... | null |
2,175 | import math
import os
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
Cau... | null |
2,176 | import torch
import torch.distributed
from torch import nn
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple
from text_generation_server.utils import paged_attention, flash_attn
from text_generation_server.utils.layers impo... | null |
2,177 | from typing import Callable, Dict, List, Optional, Union, Iterable
import numpy as np
from PIL import Image
from transformers.image_processing_utils import BaseImageProcessor, BatchFeature
from transformers.image_transforms import (
resize,
to_channel_dimension_format,
rescale,
normalize,
)
from transfo... | null |
2,178 | from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseM... | null |
2,179 | from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseM... | null |
2,180 | from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseM... | null |
2,181 | from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseM... | null |
2,182 | from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseM... | Make causal mask used for bi-directional self-attention. |
2,183 | from typing import List, Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers import PreTrainedModel
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
BaseM... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
2,184 | import math
import torch
from typing import Optional, List, Tuple
CACHE_MANAGER: Optional["CacheManager"] = None
class CacheManager:
def __init__(
self,
num_blocks: int,
num_layers: int,
num_heads: int,
head_size: int,
repeat_slots: bool,
dtype: torch.dtype,
... | null |
2,185 | import math
import torch
from typing import Optional, List, Tuple
CACHE_MANAGER: Optional["CacheManager"] = None
class CacheManager:
def __init__(
self,
num_blocks: int,
num_layers: int,
num_heads: int,
head_size: int,
repeat_slots: bool,
dtype: torch.dtype,
... | null |
2,186 | import math
import torch
import torch.distributed
import numpy as np
from dataclasses import dataclass
from opentelemetry import trace
from transformers import PreTrainedTokenizerBase
from transformers.models.llama import LlamaTokenizerFast
from typing import Optional, Tuple, Type
from text_generation_server.pb import ... | null |
2,187 | import math
import torch
import torch.distributed
import numpy as np
from dataclasses import dataclass
from opentelemetry import trace
from transformers import PreTrainedTokenizerBase
from transformers.models.llama import LlamaTokenizerFast
from typing import Optional, Tuple, Type
from text_generation_server.pb import ... | null |
2,188 | import re
import torch
import torch.distributed
from typing import List, Optional, Type
from transformers import (
AutoTokenizer,
AutoConfig,
PreTrainedTokenizerBase,
)
from text_generation_server.models import CausalLM
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_s... | Applies custom splitting to the text for GALILEO's tokenization Parameters ---------- text : str Input text to split Returns ---------- str - the text with the split token added |
2,189 | import time
import os
from datetime import timedelta
from loguru import logger
from pathlib import Path
from typing import Optional, List
from huggingface_hub import file_download, hf_api, HfApi, hf_hub_download
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE
from huggingface_hub.utils import (
LocalEnt... | Download the safetensors files from the hub |
2,190 | import datetime
import torch
import os
from loguru import logger
from pathlib import Path
from safetensors.torch import save_file, load_file, _find_shared_tensors, _is_complete
from typing import List, Dict
from collections import defaultdict
def convert_file(pt_file: Path, sf_file: Path, discard_names: List[str]):
... | null |
2,191 | import os
import torch
from loguru import logger
from text_generation_server.utils.import_utils import IS_CUDA_SYSTEM, IS_ROCM_SYSTEM
HAS_FLASH_ATTN = False
HAS_FLASH_ATTN_V2_CUDA = False
HAS_FLASH_ATTN_V2_ROCM = False
try:
try:
import flash_attn_2_cuda
except ImportError:
architecture_suffix = ... | null |
2,192 | SPECULATE = None
def get_speculate() -> int:
global SPECULATE
return SPECULATE | null |
2,193 | SPECULATE = None
def set_speculate(speculate: int):
global SPECULATE
SPECULATE = speculate | null |
2,194 | import os
import torch
from datetime import timedelta
from loguru import logger
RANK = int(os.getenv("RANK", "0"))
WORLD_SIZE = int(os.getenv("WORLD_SIZE", "1"))
MEMORY_FRACTION = float(os.getenv("CUDA_MEMORY_FRACTION", "1.0"))
class FakeGroup:
def __init__(self, rank, size):
self._rank = rank
self.... | null |
2,195 | import torch
from vllm import cache_ops
from vllm import attention_ops
def reshape_and_cache(
key: torch.Tensor,
value: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
slots: torch.Tensor,
):
cache_ops.reshape_and_cache(key, value, key_cache, value_cache, slots) | null |
2,196 | import torch
from vllm import cache_ops
from vllm import attention_ops
_PARTITION_SIZE = 512
def attention(
out: torch.Tensor,
query: torch.Tensor,
key_cache: torch.Tensor,
value_cache: torch.Tensor,
kv_head_mapping: torch.Tensor,
softmax_scale: float,
block_tables: torch.Tensor,
input_... | null |
2,197 | import re
from typing import List, Optional, Tuple
import math
import torch
from text_generation_server.pb import generate_pb2
from text_generation_server.pb.generate_pb2 import FinishReason, GrammarType
from text_generation_server.utils.logits_process import (
FrequencyPenaltyLogitsProcessor,
GrammarLogitProce... | null |
2,198 | import re
from typing import List, Optional, Tuple
import math
import torch
from text_generation_server.pb import generate_pb2
from text_generation_server.pb.generate_pb2 import FinishReason, GrammarType
from text_generation_server.utils.logits_process import (
FrequencyPenaltyLogitsProcessor,
GrammarLogitProce... | Find the top n most likely tokens for a batch of generations. When multiple tokens have equal probabilities and they don't all fit, the remaining tokens are also returned. |
2,199 | import os
import torch
import torch.distributed
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Optional
from loguru import logger
from functools import lru_cache
from accelerate import init_empty_weights
from text_generation_server.utils.gptq.quant_linear import QuantLinear
fr... | null |
2,200 | import os
import torch
import torch.distributed
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Optional
from loguru import logger
from functools import lru_cache
from accelerate import init_empty_weights
from text_generation_server.utils.gptq.quant_linear import QuantLinear
fr... | null |
2,201 | import os
import torch
import torch.distributed
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Optional
from loguru import logger
from functools import lru_cache
from accelerate import init_empty_weights
from text_generation_server.utils.gptq.quant_linear import QuantLinear
fr... | null |
2,202 | import os
import torch
import torch.distributed
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Optional
from loguru import logger
from functools import lru_cache
from accelerate import init_empty_weights
from text_generation_server.utils.gptq.quant_linear import QuantLinear
fr... | null |
2,203 | import os
import torch
import torch.distributed
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Optional
from loguru import logger
from functools import lru_cache
from accelerate import init_empty_weights
from text_generation_server.utils.gptq.quant_linear import QuantLinear
fr... | null |
2,204 | import os
import torch
import torch.distributed
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Optional
from loguru import logger
from functools import lru_cache
from accelerate import init_empty_weights
from text_generation_server.utils.gptq.quant_linear import QuantLinear
fr... | null |
2,205 | import os
import torch
import torch.distributed
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Optional
from loguru import logger
from functools import lru_cache
from accelerate import init_empty_weights
from text_generation_server.utils.gptq.quant_linear import QuantLinear
fr... | null |
2,206 | import os
import torch
import torch.distributed
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Optional
from loguru import logger
from functools import lru_cache
from accelerate import init_empty_weights
from text_generation_server.utils.gptq.quant_linear import QuantLinear
fr... | null |
2,207 | import os
import torch
import torch.distributed
from torch import nn
from torch.nn import functional as F
from typing import List, Tuple, Optional
from loguru import logger
from functools import lru_cache
from accelerate import init_empty_weights
from text_generation_server.utils.gptq.quant_linear import QuantLinear
fr... | null |
2,208 | import os
import json
from loguru import logger
import torch
from transformers import AutoTokenizer
from peft import AutoPeftModelForCausalLM, AutoPeftModelForSeq2SeqLM
def download_and_unload_peft(model_id, revision, trust_remote_code):
torch_dtype = torch.float16
logger.info("Trying to load a Peft model. It... | null |
2,209 | import builtins
import math
import time
from typing import Dict
import triton
class Autotuner(triton.KernelInterface):
def __init__(
self,
fn,
arg_names,
configs,
key,
reset_to_zero,
prune_configs_by: Dict = None,
nearest_power_of_two: bool = False,
... | Decorator for auto-tuning a :code:`triton.jit`'d function. .. highlight:: python .. code-block:: python @triton.autotune(configs=[ triton.Config(meta={'BLOCK_SIZE': 128}, num_warps=4), triton.Config(meta={'BLOCK_SIZE': 1024}, num_warps=8), ], key=['x_size'] # the two above configs will be evaluated anytime # the value ... |
2,210 | import builtins
import math
import time
from typing import Dict
import triton
The provided code snippet includes necessary dependencies for implementing the `matmul248_kernel_config_pruner` function. Write a Python function `def matmul248_kernel_config_pruner(configs, nargs)` to solve the following problem:
The main p... | The main purpose of this function is to shrink BLOCK_SIZE_* when the corresponding dimension is smaller. |
2,211 | import torch
from exllama_kernels import make_q4, q4_matmul, prepare_buffers, set_tuning_params
none_tensor = torch.empty((1, 1), device="meta")
The provided code snippet includes necessary dependencies for implementing the `ext_make_q4` function. Write a Python function `def ext_make_q4(qweight, qzeros, scales, g_idx... | Construct Q4Matrix, return handle |
2,212 | import torch
from exllama_kernels import make_q4, q4_matmul, prepare_buffers, set_tuning_params
The provided code snippet includes necessary dependencies for implementing the `ext_q4_matmul` function. Write a Python function `def ext_q4_matmul(x, q4, q4_width)` to solve the following problem:
Matrix multiplication, re... | Matrix multiplication, returns x @ q4 |
2,213 | import torch
from exllama_kernels import make_q4, q4_matmul, prepare_buffers, set_tuning_params
DEVICE = None
def set_device(device):
global DEVICE
DEVICE = device | null |
2,214 | import torch
from exllama_kernels import make_q4, q4_matmul, prepare_buffers, set_tuning_params
MAX_DQ = 1
MAX_INNER = 1
ACT_ORDER = False
DEVICE = None
TEMP_STATE = None
TEMP_DQ = None
def create_exllama_buffers(max_total_tokens: int):
global MAX_DQ, MAX_INNER, ACT_ORDER, DEVICE, TEMP_STATE, TEMP_DQ
assert D... | null |
2,215 | import torch
import torch.nn as nn
from loguru import logger
The provided code snippet includes necessary dependencies for implementing the `ext_gemm_half_q_half` function. Write a Python function `def ext_gemm_half_q_half(x, q_handle, q4_width, force_cuda)` to solve the following problem:
Matrix multiplication, retur... | Matrix multiplication, returns x @ q4 |
2,216 | import torch
import torch.nn as nn
from loguru import logger
none_tensor = torch.empty((1, 1), device="meta")
def make_group_map(q_groups, num_qrows):
gr = q_groups.tolist()
group_map = []
num_groups = len(gr) // 2
for i in range(num_groups):
bits = gr[i * 2]
if i < num_groups - 1:
... | Create Q matrix |
2,217 | import torch
import torch.nn as nn
from loguru import logger
DEVICE = None
def set_device(device):
global DEVICE
DEVICE = device | null |
2,218 | import torch
import torch.nn as nn
from loguru import logger
DEVICE = None
FIXED_BYTES = 0
LAYERS = []
class ExLlamaV2DeviceTensors:
device_idx: int
scratch_bytes: int
scratch_idx: int
scratch: torch.tensor = None
def __init__(self, device, scratch_bytes):
self.device = device
self.s... | null |
2,219 | import time
import torch.nn as nn
import math
import json
import os
import torch
import transformers
from texttable import Texttable
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
from huggingface_hub import HfApi
from accelerate import init_empty_weights
from text_generation_server.utils impo... | null |
2,220 | import math
import numpy as np
import torch
import torch.nn as nn
from torch.cuda.amp import custom_bwd, custom_fwd
try:
import triton
import triton.language as tl
from . import custom_autotune
# code based https://github.com/fpgaminer/GPTQ-triton
configs=[
triton.Config(
... | null |
2,221 | from functools import lru_cache
def log_once(log, msg: str):
log(msg) | null |
2,222 | import math
import torch
from loguru import logger
from typing import Dict, Union
from text_generation_server.pb.generate_pb2 import GrammarType
from outlines.fsm.fsm import RegexFSM
from outlines.fsm.json_schema import build_regex_from_object
from functools import lru_cache
from typing import List, Optional, DefaultDi... | null |
2,223 | import ast
def get_model_node_type(child_node) -> str:
direct_node_types_mapping = [
(ast.If, lambda n: 'if'),
(ast.Pass, lambda n: 'pass'),
((ast.Assign, ast.AnnAssign), lambda n: get_assighment_type(n)),
((ast.FunctionDef, ast.AsyncFunctionDef), lambda n: get_funcdef_type(n)),
... | null |
2,224 | from __future__ import annotations
import ast
from typing import Any
FORBIDDEN_TYPES = {
"field",
}
SKIP_SPECIAL_ATTRIBUTES = {
"__code__",
"__slots__",
}
def skip_dataclasses(class_def: ast.ClassDef) -> bool:
if class_def.decorator_list is not None:
for decorator in class_def.decorator_list:
... | null |
2,225 | from __future__ import annotations
import itertools
import logging
from collections import defaultdict
from dataclasses import dataclass
from typing import List, Union, Dict, Any
from prettytable import PrettyTable, SINGLE_BORDER
from checkov.common.bridgecrew.severities import BcSeverities
from checkov.common.models.e... | null |
2,226 | from __future__ import annotations
import logging
import re
from pathlib import Path
from typing import Any
from checkov.ansible.graph_builder.graph_components.resource_types import ResourceType
from checkov.common.parsers.yaml.parser import parse
from checkov.common.resource_code_logger_filter import add_resource_code... | Finds yaml files |
2,227 | from __future__ import annotations
import logging
import re
from pathlib import Path
from typing import Any
from checkov.ansible.graph_builder.graph_components.resource_types import ResourceType
from checkov.common.parsers.yaml.parser import parse
from checkov.common.resource_code_logger_filter import add_resource_code... | null |
2,228 | from __future__ import annotations
import logging
import re
from pathlib import Path
from typing import Any
from checkov.ansible.graph_builder.graph_components.resource_types import ResourceType
from checkov.common.parsers.yaml.parser import parse
from checkov.common.resource_code_logger_filter import add_resource_code... | null |
2,229 | from __future__ import annotations
import json
import logging
import os
import platform
from pathlib import Path
from typing import Any, Tuple
import dpath
import yaml
from jsonschema import validate, ValidationError
from checkov.common.parsers.yaml.loader import SafeLineLoaderGhaSchema
from checkov.common.parsers.yaml... | null |
2,230 | from __future__ import annotations
import json
import logging
import os
import platform
from pathlib import Path
from typing import Any, Tuple
import dpath
import yaml
from jsonschema import validate, ValidationError
from checkov.common.parsers.yaml.loader import SafeLineLoaderGhaSchema
from checkov.common.parsers.yaml... | null |
2,231 | from __future__ import annotations
import logging
import re
from collections.abc import Collection
from pathlib import Path
from typing import Any
from checkov.common.resource_code_logger_filter import add_resource_code_filter_to_logger
from checkov.terraform_json.parser import parse
TF_JSON_POSSIBLE_FILE_ENDINGS = (".... | Finds Terraform JSON files |
2,232 | from __future__ import annotations
import logging
import re
from collections.abc import Collection
from pathlib import Path
from typing import Any
from checkov.common.resource_code_logger_filter import add_resource_code_filter_to_logger
from checkov.terraform_json.parser import parse
logger = logging.getLogger(__name__... | Creates dict objects and code lines for given files |
2,233 | from __future__ import annotations
import os
import re
import inspect
from typing import List, Optional, Tuple, Union
from tabulate import tabulate
from checkov.ansible.checks.registry import registry as ansible_registry
from checkov.argo_workflows.checks.registry import registry as argo_workflows_registry
from checkov... | null |
2,234 | import ctypes
from datetime import datetime
import json
import logging
import os
import platform
import re
import stat
from pathlib import Path
from typing import Optional, List, Set, Union, Dict, Any, Tuple, cast
from cachetools import cached, TTLCache
from pydantic import ValidationError
from checkov.common.bridgecre... | null |
2,235 | import ctypes
from datetime import datetime
import json
import logging
import os
import platform
import re
import stat
from pathlib import Path
from typing import Optional, List, Set, Union, Dict, Any, Tuple, cast
from cachetools import cached, TTLCache
from pydantic import ValidationError
from checkov.common.bridgecre... | null |
2,236 | import ctypes
from datetime import datetime
import json
import logging
import os
import platform
import re
import stat
from pathlib import Path
from typing import Optional, List, Set, Union, Dict, Any, Tuple, cast
from cachetools import cached, TTLCache
from pydantic import ValidationError
from checkov.common.bridgecre... | null |
2,237 | from __future__ import annotations
from typing import List, Tuple
def cut_code_block_ident(code_block: List[Tuple[int, str]]) -> List[Tuple[int, str]]:
min_ident = len(code_block[0][1]) - len(code_block[0][1].lstrip())
for item in code_block[1:]:
current_min_ident = len(item[1]) - len(item[1].lstrip())
... | null |
2,238 | from __future__ import annotations
import logging
import os
from typing import Type, Any, TYPE_CHECKING
from typing_extensions import TypeAlias
from checkov.common.checks_infra.registry import get_graph_checks_registry
from checkov.common.graph.checks_infra.registry import BaseRegistry
from checkov.common.typing impor... | null |
2,239 | from __future__ import annotations
import logging
import os
from typing import Type, Any, TYPE_CHECKING
from typing_extensions import TypeAlias
from checkov.common.checks_infra.registry import get_graph_checks_registry
from checkov.common.graph.checks_infra.registry import BaseRegistry
from checkov.common.typing impor... | null |
2,240 | from __future__ import annotations
import logging
import os
from typing import Type, Any, TYPE_CHECKING
from typing_extensions import TypeAlias
from checkov.common.checks_infra.registry import get_graph_checks_registry
from checkov.common.graph.checks_infra.registry import BaseRegistry
from checkov.common.typing impor... | null |
2,241 | from __future__ import annotations
from typing import Any
def extract_commands(conf: dict[str, Any]) -> tuple[list[str], list[str]]:
commands = conf.get("command")
if not commands or not isinstance(commands, list):
return [], []
values = []
keys = []
for cmd in commands:
if cmd is N... | null |
2,242 | from __future__ import annotations
import logging
import os
from typing import Dict, Any, TYPE_CHECKING
import dpath
from checkov.common.models.enums import CheckResult
from checkov.common.util.consts import LINE_FIELD_NAMES, START_LINE, END_LINE
from checkov.runner_filter import RunnerFilter
from checkov.common.bridge... | null |
2,243 | from __future__ import annotations
import logging
import os
from typing import Dict, Any, TYPE_CHECKING
import dpath
from checkov.common.models.enums import CheckResult
from checkov.common.util.consts import LINE_FIELD_NAMES, START_LINE, END_LINE
from checkov.runner_filter import RunnerFilter
from checkov.common.bridge... | null |
2,244 | from __future__ import annotations
import logging
import os
from typing import Dict, Any, TYPE_CHECKING
import dpath
from checkov.common.models.enums import CheckResult
from checkov.common.util.consts import LINE_FIELD_NAMES, START_LINE, END_LINE
from checkov.runner_filter import RunnerFilter
from checkov.common.bridge... | null |
2,245 | from __future__ import annotations
import logging
import os
from typing import Dict, Any, TYPE_CHECKING
import dpath
from checkov.common.models.enums import CheckResult
from checkov.common.util.consts import LINE_FIELD_NAMES, START_LINE, END_LINE
from checkov.runner_filter import RunnerFilter
from checkov.common.bridge... | null |
2,246 | from __future__ import annotations
import logging
import os
from typing import Dict, Any, TYPE_CHECKING
import dpath
from checkov.common.models.enums import CheckResult
from checkov.common.util.consts import LINE_FIELD_NAMES, START_LINE, END_LINE
from checkov.runner_filter import RunnerFilter
from checkov.common.bridge... | Creates a cleaned version of check_result for further usage |
2,247 | from __future__ import annotations
from typing import TYPE_CHECKING, Any
from checkov.common.util.data_structures_utils import find_in_dict
from checkov.kubernetes.image_referencer.base_provider import BaseKubernetesProvider
def _extract_images_from_spec(spec: dict[str, Any] | None) -> list[str]:
image_names: set[s... | null |
2,248 | from __future__ import annotations
from typing import TYPE_CHECKING, Any
from checkov.common.util.data_structures_utils import find_in_dict
from checkov.kubernetes.image_referencer.base_provider import BaseKubernetesProvider
def _extract_images_from_spec(spec: dict[str, Any] | None) -> list[str]:
image_names: set[s... | null |
2,249 | from __future__ import annotations
from typing import TYPE_CHECKING, Any
from checkov.common.util.data_structures_utils import find_in_dict
from checkov.kubernetes.image_referencer.base_provider import BaseKubernetesProvider
def _extract_images_from_spec(spec: dict[str, Any] | None) -> list[str]:
def find_in_dict(inpu... | null |
2,250 | from __future__ import annotations
from typing import TYPE_CHECKING, Any
from checkov.common.util.data_structures_utils import find_in_dict
from checkov.kubernetes.image_referencer.base_provider import BaseKubernetesProvider
def _extract_images_from_spec(spec: dict[str, Any] | None) -> list[str]:
image_names: set[s... | null |
2,251 | from __future__ import annotations
from pathlib import Path
from typing import cast, List, Tuple, Dict, Any, TYPE_CHECKING
from checkov.common.util.suppression import collect_suppressions_for_report
BICEP_COMMENT = "//"
DEFINITIONS_KEYS_TO_PARSE = {"parameters": "parameters", "resources": "resources"}
def collect_supp... | null |
2,252 | from __future__ import annotations
import os
from pathlib import Path
from typing import Any, TYPE_CHECKING, cast
from checkov.bicep.graph_builder.graph_components.block_types import BlockType, BlockTypeAlias
from checkov.bicep.graph_builder.local_graph import BicepElements, BicepElementsAlias
BLOCK_TYPE_TO_BICEP_ELEME... | null |
2,253 | from __future__ import annotations
import logging
import os
import re
from collections.abc import Collection
from pathlib import Path
from typing import Any, TYPE_CHECKING
from checkov.common.runners.base_runner import filter_ignored_paths
from checkov.runner_filter import RunnerFilter
from checkov.bicep.parser import ... | Finds Bicep files |
2,254 | from __future__ import annotations
import logging
import os
import re
from collections.abc import Collection
from pathlib import Path
from typing import Any, TYPE_CHECKING
from checkov.common.runners.base_runner import filter_ignored_paths
from checkov.runner_filter import RunnerFilter
from checkov.bicep.parser import ... | null |
2,255 | from __future__ import annotations
import logging
import os
import re
from collections.abc import Collection
from pathlib import Path
from typing import Any, TYPE_CHECKING
from checkov.common.runners.base_runner import filter_ignored_paths
from checkov.runner_filter import RunnerFilter
from checkov.bicep.parser import ... | null |
2,256 | from __future__ import annotations
import logging
import os
import re
from collections.abc import Collection
from pathlib import Path
from typing import Any, TYPE_CHECKING
from checkov.common.runners.base_runner import filter_ignored_paths
from checkov.runner_filter import RunnerFilter
from checkov.bicep.parser import ... | Adjusts the value, if the 'element_name' references a nested key Ex: element_name = publicKey.keyData value = {"keyData": "key-data", "path": "path"} returns new_value = "key-data" |
2,257 | from __future__ import annotations
from typing import TYPE_CHECKING, Any
from checkov.bicep.image_referencer.base_provider import BaseBicepProvider
from checkov.common.util.data_structures_utils import find_in_dict
from checkov.common.util.type_forcers import force_list
def find_in_dict(input_dict: dict[str, Any], key... | null |
2,258 | from __future__ import annotations
from typing import TYPE_CHECKING, Any
from checkov.bicep.image_referencer.base_provider import BaseBicepProvider
from checkov.common.util.data_structures_utils import find_in_dict
from checkov.common.util.type_forcers import force_list
def find_in_dict(input_dict: dict[str, Any], key... | null |
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