repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1 value | license stringclasses 7 values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2 classes |
|---|---|---|---|---|---|---|---|---|
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/data/processor/processor_utils.py | src/llamafactory/data/processor/processor_utils.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import bisect
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Optional
if TYPE_CHECKING:
from transformers import PreTrainedTokenizer, ProcessorMixin
from ...hparams import DataArguments
from ..template import Template
@dataclass
class DatasetProcessor(ABC):
r"""A class for data processors."""
template: "Template"
tokenizer: "PreTrainedTokenizer"
processor: Optional["ProcessorMixin"]
data_args: "DataArguments"
@abstractmethod
def preprocess_dataset(self, examples: dict[str, list[Any]]) -> dict[str, list[Any]]:
r"""Build model inputs from the examples."""
...
@abstractmethod
def print_data_example(self, example: dict[str, list[int]]) -> None:
r"""Print a data example to stdout."""
...
def search_for_fit(numbers: list[int], capacity: int) -> int:
r"""Find the index of largest number that fits into the knapsack with the given capacity."""
index = bisect.bisect(numbers, capacity)
return -1 if index == 0 else (index - 1)
def greedy_knapsack(numbers: list[int], capacity: int) -> list[list[int]]:
r"""Implement efficient greedy algorithm with binary search for the knapsack problem."""
numbers.sort() # sort numbers in ascending order for binary search
knapsacks = []
while numbers:
current_knapsack = []
remaining_capacity = capacity
while True:
index = search_for_fit(numbers, remaining_capacity)
if index == -1:
break # no more numbers fit in this knapsack
remaining_capacity -= numbers[index] # update the remaining capacity
current_knapsack.append(numbers.pop(index)) # add the number to knapsack
knapsacks.append(current_knapsack)
return knapsacks
def infer_seqlen(source_len: int, target_len: int, cutoff_len: int) -> tuple[int, int]:
r"""Compute the real sequence length after truncation by the cutoff_len."""
if target_len * 2 < cutoff_len: # truncate source
max_target_len = cutoff_len
elif source_len * 2 < cutoff_len: # truncate target
max_target_len = cutoff_len - source_len
else: # truncate both
max_target_len = int(cutoff_len * (target_len / (source_len + target_len)))
new_target_len = min(max_target_len, target_len)
max_source_len = max(cutoff_len - new_target_len, 0)
new_source_len = min(max_source_len, source_len)
return new_source_len, new_target_len
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/data/processor/unsupervised.py | src/llamafactory/data/processor/unsupervised.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Optional
from ...extras import logging
from ..data_utils import Role
from .processor_utils import DatasetProcessor, infer_seqlen
if TYPE_CHECKING:
from ..mm_plugin import AudioInput, ImageInput, VideoInput
logger = logging.get_logger(__name__)
class UnsupervisedDatasetProcessor(DatasetProcessor):
def _encode_data_example(
self,
prompt: list[dict[str, str]],
response: list[dict[str, str]],
system: Optional[str],
tools: Optional[str],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
) -> tuple[list[int], list[int]]:
if len(response) == 1:
messages = prompt + response
else:
messages = prompt + [{"role": Role.ASSISTANT.value, "content": ""}]
messages = self.template.mm_plugin.process_messages(messages, images, videos, audios, self.processor)
input_ids, labels = self.template.encode_oneturn(self.tokenizer, messages, system, tools)
if self.template.efficient_eos:
labels += [self.tokenizer.eos_token_id]
input_ids, _ = self.template.mm_plugin.process_token_ids(
input_ids, None, images, videos, audios, self.tokenizer, self.processor
)
source_len, target_len = infer_seqlen(len(input_ids), len(labels), self.data_args.cutoff_len)
input_ids = input_ids[:source_len]
labels = labels[:target_len]
return input_ids, labels
def preprocess_dataset(self, examples: dict[str, list[Any]]) -> dict[str, list[Any]]:
# build inputs with format `<bos> X` and labels with format `Y <eos>`
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1:
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
input_ids, labels = self._encode_data_example(
prompt=examples["_prompt"][i],
response=examples["_response"][i],
system=examples["_system"][i],
tools=examples["_tools"][i],
images=examples["_images"][i] or [],
videos=examples["_videos"][i] or [],
audios=examples["_audios"][i] or [],
)
model_inputs["input_ids"].append(input_ids)
model_inputs["attention_mask"].append([1] * len(input_ids))
model_inputs["labels"].append(labels)
model_inputs["images"].append(examples["_images"][i])
model_inputs["videos"].append(examples["_videos"][i])
model_inputs["audios"].append(examples["_audios"][i])
return model_inputs
def print_data_example(self, example: dict[str, list[int]]) -> None:
print("input_ids:\n{}".format(example["input_ids"]))
print("inputs:\n{}".format(self.tokenizer.decode(example["input_ids"], skip_special_tokens=False)))
print("label_ids:\n{}".format(example["labels"]))
print("labels:\n{}".format(self.tokenizer.decode(example["labels"], skip_special_tokens=False)))
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/data/processor/__init__.py | src/llamafactory/data/processor/__init__.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .feedback import FeedbackDatasetProcessor
from .pairwise import PairwiseDatasetProcessor
from .pretrain import PretrainDatasetProcessor
from .processor_utils import DatasetProcessor
from .supervised import PackedSupervisedDatasetProcessor, SupervisedDatasetProcessor
from .unsupervised import UnsupervisedDatasetProcessor
__all__ = [
"DatasetProcessor",
"FeedbackDatasetProcessor",
"PackedSupervisedDatasetProcessor",
"PairwiseDatasetProcessor",
"PretrainDatasetProcessor",
"SupervisedDatasetProcessor",
"UnsupervisedDatasetProcessor",
]
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/data/processor/pairwise.py | src/llamafactory/data/processor/pairwise.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import defaultdict
from typing import TYPE_CHECKING, Any, Optional
from ...extras import logging
from ...extras.constants import IGNORE_INDEX
from .processor_utils import DatasetProcessor, infer_seqlen
if TYPE_CHECKING:
from ..mm_plugin import AudioInput, ImageInput, VideoInput
logger = logging.get_logger(__name__)
class PairwiseDatasetProcessor(DatasetProcessor):
def _encode_data_example(
self,
prompt: list[dict[str, str]],
response: list[dict[str, str]],
system: Optional[str],
tools: Optional[str],
images: list["ImageInput"],
videos: list["VideoInput"],
audios: list["AudioInput"],
) -> tuple[list[int], list[int], list[int], list[int]]:
chosen_messages = self.template.mm_plugin.process_messages(
prompt + [response[0]], images, videos, audios, self.processor
)
rejected_messages = self.template.mm_plugin.process_messages(
prompt + [response[1]], images, videos, audios, self.processor
)
prompt_ids, chosen_ids = self.template.encode_oneturn(self.tokenizer, chosen_messages, system, tools)
_, rejected_ids = self.template.encode_oneturn(self.tokenizer, rejected_messages, system, tools)
if self.template.efficient_eos:
chosen_ids += [self.tokenizer.eos_token_id]
rejected_ids += [self.tokenizer.eos_token_id]
prompt_ids, _ = self.template.mm_plugin.process_token_ids(
prompt_ids, None, images, videos, audios, self.tokenizer, self.processor
)
# consider the response is more important
source_len, target_len = infer_seqlen(
len(prompt_ids), max(len(chosen_ids), len(rejected_ids)), self.data_args.cutoff_len
)
prompt_ids = prompt_ids[:source_len]
chosen_ids = chosen_ids[:target_len]
rejected_ids = rejected_ids[:target_len]
chosen_input_ids = prompt_ids + chosen_ids
chosen_labels = [IGNORE_INDEX] * source_len + chosen_ids
rejected_input_ids = prompt_ids + rejected_ids
rejected_labels = [IGNORE_INDEX] * source_len + rejected_ids
return chosen_input_ids, chosen_labels, rejected_input_ids, rejected_labels
def preprocess_dataset(self, examples: dict[str, list[Any]]) -> dict[str, list[Any]]:
# build input pairs with format `<bos> X`, `Y1 <eos>` and `Y2 <eos>`
model_inputs = defaultdict(list)
for i in range(len(examples["_prompt"])):
if len(examples["_prompt"][i]) % 2 != 1 or len(examples["_response"][i]) < 2:
logger.warning_rank0(
"Dropped invalid example: {}".format(examples["_prompt"][i] + examples["_response"][i])
)
continue
chosen_input_ids, chosen_labels, rejected_input_ids, rejected_labels = self._encode_data_example(
prompt=examples["_prompt"][i],
response=examples["_response"][i],
system=examples["_system"][i],
tools=examples["_tools"][i],
images=examples["_images"][i] or [],
videos=examples["_videos"][i] or [],
audios=examples["_audios"][i] or [],
)
model_inputs["chosen_input_ids"].append(chosen_input_ids)
model_inputs["chosen_attention_mask"].append([1] * len(chosen_input_ids))
model_inputs["chosen_labels"].append(chosen_labels)
model_inputs["rejected_input_ids"].append(rejected_input_ids)
model_inputs["rejected_attention_mask"].append([1] * len(rejected_input_ids))
model_inputs["rejected_labels"].append(rejected_labels)
model_inputs["images"].append(examples["_images"][i])
model_inputs["videos"].append(examples["_videos"][i])
model_inputs["audios"].append(examples["_audios"][i])
return model_inputs
def print_data_example(self, example: dict[str, list[int]]) -> None:
valid_chosen_labels = list(filter(lambda x: x != IGNORE_INDEX, example["chosen_labels"]))
valid_rejected_labels = list(filter(lambda x: x != IGNORE_INDEX, example["rejected_labels"]))
print("chosen_input_ids:\n{}".format(example["chosen_input_ids"]))
print(
"chosen_inputs:\n{}".format(self.tokenizer.decode(example["chosen_input_ids"], skip_special_tokens=False))
)
print("chosen_label_ids:\n{}".format(example["chosen_labels"]))
print(f"chosen_labels:\n{self.tokenizer.decode(valid_chosen_labels, skip_special_tokens=False)}")
print("rejected_input_ids:\n{}".format(example["rejected_input_ids"]))
print(
"rejected_inputs:\n{}".format(
self.tokenizer.decode(example["rejected_input_ids"], skip_special_tokens=False)
)
)
print("rejected_label_ids:\n{}".format(example["rejected_labels"]))
print(f"rejected_labels:\n{self.tokenizer.decode(valid_rejected_labels, skip_special_tokens=False)}")
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/extras/constants.py | src/llamafactory/extras/constants.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from collections import OrderedDict, defaultdict
from enum import Enum, unique
from peft.utils import SAFETENSORS_WEIGHTS_NAME as SAFE_ADAPTER_WEIGHTS_NAME
from peft.utils import WEIGHTS_NAME as ADAPTER_WEIGHTS_NAME
from transformers.utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, WEIGHTS_NAME
AUDIO_PLACEHOLDER = os.getenv("AUDIO_PLACEHOLDER", "<audio>")
CHECKPOINT_NAMES = {
SAFE_ADAPTER_WEIGHTS_NAME,
ADAPTER_WEIGHTS_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
}
CHOICES = ["A", "B", "C", "D"]
DATA_CONFIG = "dataset_info.json"
DEFAULT_TEMPLATE = defaultdict(str)
FILEEXT2TYPE = {
"arrow": "arrow",
"csv": "csv",
"json": "json",
"jsonl": "json",
"parquet": "parquet",
"txt": "text",
}
IGNORE_INDEX = -100
IMAGE_PLACEHOLDER = os.getenv("IMAGE_PLACEHOLDER", "<image>")
LAYERNORM_NAMES = {"norm", "ln"}
LLAMABOARD_CONFIG = "llamaboard_config.yaml"
MCA_SUPPORTED_MODELS = {
"deepseek_v3",
"llama",
"mistral",
"mixtral",
"qwen2",
"qwen2_vl",
"qwen2_5_vl",
"qwen3_vl",
"qwen3",
"qwen3_moe",
"qwen3_next",
}
METHODS = ["full", "freeze", "lora", "oft"]
MOD_SUPPORTED_MODELS = {"bloom", "falcon", "gemma", "llama", "mistral", "mixtral", "phi", "starcoder2"}
MULTIMODAL_SUPPORTED_MODELS = set()
PEFT_METHODS = {"lora", "oft"}
RUNNING_LOG = "running_log.txt"
SUBJECTS = ["Average", "STEM", "Social Sciences", "Humanities", "Other"]
SUPPORTED_MODELS = OrderedDict()
TRAINER_LOG = "trainer_log.jsonl"
TRAINING_ARGS = "training_args.yaml"
TRAINING_STAGES = {
"Supervised Fine-Tuning": "sft",
"Reward Modeling": "rm",
"PPO": "ppo",
"DPO": "dpo",
"KTO": "kto",
"Pre-Training": "pt",
}
STAGES_USE_PAIR_DATA = {"rm", "dpo"}
SUPPORTED_CLASS_FOR_S2ATTN = {"llama"}
SWANLAB_CONFIG = "swanlab_public_config.json"
VIDEO_PLACEHOLDER = os.getenv("VIDEO_PLACEHOLDER", "<video>")
V_HEAD_WEIGHTS_NAME = "value_head.bin"
V_HEAD_SAFE_WEIGHTS_NAME = "value_head.safetensors"
class AttentionFunction(str, Enum):
AUTO = "auto"
DISABLED = "disabled"
SDPA = "sdpa"
FA2 = "fa2"
FA3 = "fa3"
class EngineName(str, Enum):
HF = "huggingface"
VLLM = "vllm"
SGLANG = "sglang"
KT = "ktransformers"
class DownloadSource(str, Enum):
DEFAULT = "hf"
MODELSCOPE = "ms"
OPENMIND = "om"
@unique
class QuantizationMethod(str, Enum):
r"""Borrowed from `transformers.utils.quantization_config.QuantizationMethod`."""
BNB = "bnb"
GPTQ = "gptq"
AWQ = "awq"
AQLM = "aqlm"
QUANTO = "quanto"
EETQ = "eetq"
HQQ = "hqq"
MXFP4 = "mxfp4"
FP8 = "fp8"
class RopeScaling(str, Enum):
LINEAR = "linear"
DYNAMIC = "dynamic"
YARN = "yarn"
LLAMA3 = "llama3"
def register_model_group(
models: dict[str, dict[DownloadSource, str]],
template: str | None = None,
multimodal: bool = False,
) -> None:
for name, path in models.items():
SUPPORTED_MODELS[name] = path
if template is not None and (
any(suffix in name for suffix in ("-Chat", "-Distill", "-Instruct", "-Thinking")) or multimodal
):
DEFAULT_TEMPLATE[name] = template
if multimodal:
MULTIMODAL_SUPPORTED_MODELS.add(name)
register_model_group(
models={
"Aya-23-8B-Chat": {
DownloadSource.DEFAULT: "CohereForAI/aya-23-8B",
},
"Aya-23-35B-Chat": {
DownloadSource.DEFAULT: "CohereForAI/aya-23-35B",
},
},
template="cohere",
)
register_model_group(
models={
"Baichuan-7B-Base": {
DownloadSource.DEFAULT: "baichuan-inc/Baichuan-7B",
DownloadSource.MODELSCOPE: "baichuan-inc/baichuan-7B",
},
"Baichuan-13B-Base": {
DownloadSource.DEFAULT: "baichuan-inc/Baichuan-13B-Base",
DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan-13B-Base",
},
"Baichuan-13B-Chat": {
DownloadSource.DEFAULT: "baichuan-inc/Baichuan-13B-Chat",
DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan-13B-Chat",
},
},
template="baichuan",
)
register_model_group(
models={
"Baichuan2-7B-Base": {
DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-7B-Base",
DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-7B-Base",
},
"Baichuan2-13B-Base": {
DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-13B-Base",
DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-13B-Base",
DownloadSource.OPENMIND: "Baichuan/Baichuan2_13b_base_pt",
},
"Baichuan2-7B-Chat": {
DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-7B-Chat",
DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-7B-Chat",
DownloadSource.OPENMIND: "Baichuan/Baichuan2_7b_chat_pt",
},
"Baichuan2-13B-Chat": {
DownloadSource.DEFAULT: "baichuan-inc/Baichuan2-13B-Chat",
DownloadSource.MODELSCOPE: "baichuan-inc/Baichuan2-13B-Chat",
DownloadSource.OPENMIND: "Baichuan/Baichuan2_13b_chat_pt",
},
},
template="baichuan2",
)
register_model_group(
models={
"BLOOM-560M": {
DownloadSource.DEFAULT: "bigscience/bloom-560m",
DownloadSource.MODELSCOPE: "AI-ModelScope/bloom-560m",
},
"BLOOM-3B": {
DownloadSource.DEFAULT: "bigscience/bloom-3b",
DownloadSource.MODELSCOPE: "AI-ModelScope/bloom-3b",
},
"BLOOM-7B1": {
DownloadSource.DEFAULT: "bigscience/bloom-7b1",
DownloadSource.MODELSCOPE: "AI-ModelScope/bloom-7b1",
},
},
)
register_model_group(
models={
"BLOOMZ-560M": {
DownloadSource.DEFAULT: "bigscience/bloomz-560m",
DownloadSource.MODELSCOPE: "AI-ModelScope/bloomz-560m",
},
"BLOOMZ-3B": {
DownloadSource.DEFAULT: "bigscience/bloomz-3b",
DownloadSource.MODELSCOPE: "AI-ModelScope/bloomz-3b",
},
"BLOOMZ-7B1-mt": {
DownloadSource.DEFAULT: "bigscience/bloomz-7b1-mt",
DownloadSource.MODELSCOPE: "AI-ModelScope/bloomz-7b1-mt",
},
},
)
register_model_group(
models={
"BlueLM-7B-Base": {
DownloadSource.DEFAULT: "vivo-ai/BlueLM-7B-Base",
DownloadSource.MODELSCOPE: "vivo-ai/BlueLM-7B-Base",
},
"BlueLM-7B-Chat": {
DownloadSource.DEFAULT: "vivo-ai/BlueLM-7B-Chat",
DownloadSource.MODELSCOPE: "vivo-ai/BlueLM-7B-Chat",
},
},
template="bluelm",
)
register_model_group(
models={
"Breeze-7B": {
DownloadSource.DEFAULT: "MediaTek-Research/Breeze-7B-Base-v1_0",
},
"Breeze-7B-Instruct": {
DownloadSource.DEFAULT: "MediaTek-Research/Breeze-7B-Instruct-v1_0",
},
},
template="breeze",
)
register_model_group(
models={
"ChatGLM2-6B-Chat": {
DownloadSource.DEFAULT: "zai-org/chatglm2-6b",
DownloadSource.MODELSCOPE: "ZhipuAI/chatglm2-6b",
}
},
template="chatglm2",
)
register_model_group(
models={
"ChatGLM3-6B-Base": {
DownloadSource.DEFAULT: "zai-org/chatglm3-6b-base",
DownloadSource.MODELSCOPE: "ZhipuAI/chatglm3-6b-base",
},
"ChatGLM3-6B-Chat": {
DownloadSource.DEFAULT: "zai-org/chatglm3-6b",
DownloadSource.MODELSCOPE: "ZhipuAI/chatglm3-6b",
},
},
template="chatglm3",
)
register_model_group(
models={
"Chinese-Llama-2-1.3B": {
DownloadSource.DEFAULT: "hfl/chinese-llama-2-1.3b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-llama-2-1.3b",
},
"Chinese-Llama-2-7B": {
DownloadSource.DEFAULT: "hfl/chinese-llama-2-7b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-llama-2-7b",
},
"Chinese-Llama-2-13B": {
DownloadSource.DEFAULT: "hfl/chinese-llama-2-13b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-llama-2-13b",
},
"Chinese-Alpaca-2-1.3B-Chat": {
DownloadSource.DEFAULT: "hfl/chinese-alpaca-2-1.3b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-alpaca-2-1.3b",
},
"Chinese-Alpaca-2-7B-Chat": {
DownloadSource.DEFAULT: "hfl/chinese-alpaca-2-7b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-alpaca-2-7b",
},
"Chinese-Alpaca-2-13B-Chat": {
DownloadSource.DEFAULT: "hfl/chinese-alpaca-2-13b",
DownloadSource.MODELSCOPE: "AI-ModelScope/chinese-alpaca-2-13b",
},
},
template="llama2_zh",
)
register_model_group(
models={
"CodeGeeX4-9B-Chat": {
DownloadSource.DEFAULT: "zai-org/codegeex4-all-9b",
DownloadSource.MODELSCOPE: "ZhipuAI/codegeex4-all-9b",
},
},
template="codegeex4",
)
register_model_group(
models={
"CodeGemma-7B": {
DownloadSource.DEFAULT: "google/codegemma-7b",
},
"CodeGemma-7B-Instruct": {
DownloadSource.DEFAULT: "google/codegemma-7b-it",
DownloadSource.MODELSCOPE: "AI-ModelScope/codegemma-7b-it",
},
"CodeGemma-1.1-2B": {
DownloadSource.DEFAULT: "google/codegemma-1.1-2b",
},
"CodeGemma-1.1-7B-Instruct": {
DownloadSource.DEFAULT: "google/codegemma-1.1-7b-it",
},
},
template="gemma",
)
register_model_group(
models={
"Codestral-22B-v0.1-Chat": {
DownloadSource.DEFAULT: "mistralai/Codestral-22B-v0.1",
DownloadSource.MODELSCOPE: "swift/Codestral-22B-v0.1",
},
},
template="mistral",
)
register_model_group(
models={
"CommandR-35B-Chat": {
DownloadSource.DEFAULT: "CohereForAI/c4ai-command-r-v01",
DownloadSource.MODELSCOPE: "AI-ModelScope/c4ai-command-r-v01",
},
"CommandR-Plus-104B-Chat": {
DownloadSource.DEFAULT: "CohereForAI/c4ai-command-r-plus",
DownloadSource.MODELSCOPE: "AI-ModelScope/c4ai-command-r-plus",
},
"CommandR-35B-4bit-Chat": {
DownloadSource.DEFAULT: "CohereForAI/c4ai-command-r-v01-4bit",
DownloadSource.MODELSCOPE: "mirror013/c4ai-command-r-v01-4bit",
},
"CommandR-Plus-104B-4bit-Chat": {
DownloadSource.DEFAULT: "CohereForAI/c4ai-command-r-plus-4bit",
},
},
template="cohere",
)
register_model_group(
models={
"DBRX-132B-Base": {
DownloadSource.DEFAULT: "databricks/dbrx-base",
DownloadSource.MODELSCOPE: "AI-ModelScope/dbrx-base",
},
"DBRX-132B-Instruct": {
DownloadSource.DEFAULT: "databricks/dbrx-instruct",
DownloadSource.MODELSCOPE: "AI-ModelScope/dbrx-instruct",
},
},
template="dbrx",
)
register_model_group(
models={
"DeepSeek-LLM-7B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-llm-7b-base",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-llm-7b-base",
},
"DeepSeek-LLM-67B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-llm-67b-base",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-llm-67b-base",
},
"DeepSeek-LLM-7B-Chat": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-llm-7b-chat",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-llm-7b-chat",
},
"DeepSeek-LLM-67B-Chat": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-llm-67b-chat",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-llm-67b-chat",
},
"DeepSeek-Math-7B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-math-7b-base",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-math-7b-base",
},
"DeepSeek-Math-7B-Instruct": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-math-7b-instruct",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-math-7b-instruct",
},
"DeepSeek-MoE-16B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-moe-16b-base",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-moe-16b-base",
},
"DeepSeek-MoE-16B-Chat": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-moe-16b-chat",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-moe-16b-chat",
},
"DeepSeek-V2-16B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2-Lite",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2-Lite",
},
"DeepSeek-V2-236B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2",
},
"DeepSeek-V2-16B-Chat": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2-Lite-Chat",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2-Lite-Chat",
},
"DeepSeek-V2-236B-Chat": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2-Chat",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2-Chat",
},
"DeepSeek-Coder-V2-16B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Lite-Base",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-Coder-V2-Lite-Base",
},
"DeepSeek-Coder-V2-236B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Base",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-Coder-V2-Base",
},
"DeepSeek-Coder-V2-16B-Instruct": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
},
"DeepSeek-Coder-V2-236B-Instruct": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-Coder-V2-Instruct",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-Coder-V2-Instruct",
},
},
template="deepseek",
)
register_model_group(
models={
"DeepSeek-Coder-6.7B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-6.7b-base",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-6.7b-base",
},
"DeepSeek-Coder-7B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-7b-base-v1.5",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-7b-base-v1.5",
},
"DeepSeek-Coder-33B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-33b-base",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-33b-base",
},
"DeepSeek-Coder-6.7B-Instruct": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-6.7b-instruct",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-6.7b-instruct",
},
"DeepSeek-Coder-7B-Instruct": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-7b-instruct-v1.5",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-7b-instruct-v1.5",
},
"DeepSeek-Coder-33B-Instruct": {
DownloadSource.DEFAULT: "deepseek-ai/deepseek-coder-33b-instruct",
DownloadSource.MODELSCOPE: "deepseek-ai/deepseek-coder-33b-instruct",
},
},
template="deepseekcoder",
)
register_model_group(
models={
"DeepSeek-V2-0628-236B-Chat": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2-Chat-0628",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2-Chat-0628",
},
"DeepSeek-V2.5-236B-Chat": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2.5",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2.5",
},
"DeepSeek-V2.5-1210-236B-Chat": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V2.5-1210",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V2.5-1210",
},
"DeepSeek-V3-671B-Base": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V3-Base",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V3-Base",
},
"DeepSeek-V3-671B-Chat": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V3",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V3",
},
"DeepSeek-V3-0324-671B-Chat": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-V3-0324",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-V3-0324",
},
},
template="deepseek3",
)
register_model_group(
models={
"DeepSeek-R1-1.5B-Distill": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
},
"DeepSeek-R1-7B-Distill": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
},
"DeepSeek-R1-8B-Distill": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
},
"DeepSeek-R1-14B-Distill": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
},
"DeepSeek-R1-32B-Distill": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
},
"DeepSeek-R1-70B-Distill": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
},
"DeepSeek-R1-671B-Chat-Zero": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-Zero",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-Zero",
},
"DeepSeek-R1-671B-Chat": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1",
},
"DeepSeek-R1-0528-8B-Distill": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B",
},
"DeepSeek-R1-0528-671B-Chat": {
DownloadSource.DEFAULT: "deepseek-ai/DeepSeek-R1-0528",
DownloadSource.MODELSCOPE: "deepseek-ai/DeepSeek-R1-0528",
},
},
template="deepseekr1",
)
register_model_group(
models={
"Devstral-Small-2507-Instruct": {
DownloadSource.DEFAULT: "mistralai/Devstral-Small-2507",
DownloadSource.MODELSCOPE: "mistralai/Devstral-Small-2507",
},
},
template="mistral_small",
)
register_model_group(
models={
"dots.ocr": {
DownloadSource.DEFAULT: "rednote-hilab/dots.ocr",
DownloadSource.MODELSCOPE: "rednote-hilab/dots.ocr",
},
},
template="dots_ocr",
multimodal=True,
)
register_model_group(
models={
"ERNIE-4.5-21B-A3B-Thinking": {
DownloadSource.DEFAULT: "baidu/ERNIE-4.5-21B-A3B-Thinking",
DownloadSource.MODELSCOPE: "PaddlePaddle/ERNIE-4.5-21B-A3B-Thinking",
},
},
template="ernie",
)
register_model_group(
models={
"ERNIE-4.5-0.3B-PT": {
DownloadSource.DEFAULT: "baidu/ERNIE-4.5-0.3B-PT",
DownloadSource.MODELSCOPE: "PaddlePaddle/ERNIE-4.5-0.3B-PT",
},
"ERNIE-4.5-21B-A3B-PT": {
DownloadSource.DEFAULT: "baidu/ERNIE-4.5-21B-A3B-PT",
DownloadSource.MODELSCOPE: "PaddlePaddle/ERNIE-4.5-21B-A3B-PT",
},
"ERNIE-4.5-300B-A47B-PT": {
DownloadSource.DEFAULT: "baidu/ERNIE-4.5-300B-A47B-PT",
DownloadSource.MODELSCOPE: "PaddlePaddle/ERNIE-4.5-300B-A47B-PT",
},
},
template="ernie_nothink",
)
register_model_group(
models={
"ERNIE-4.5-VL-28B-A3B-PT": {
DownloadSource.DEFAULT: "baidu/ERNIE-4.5-VL-28B-A3B-PT",
DownloadSource.MODELSCOPE: "PaddlePaddle/ERNIE-4.5-VL-28B-A3B-PT",
},
"ERNIE-4.5-VL-28B-A3B-Thinking": {
DownloadSource.DEFAULT: "baidu/ERNIE-4.5-VL-28B-A3B-Thinking",
DownloadSource.MODELSCOPE: "PaddlePaddle/ERNIE-4.5-VL-28B-A3B-Thinking",
},
"ERNIE-4.5-VL-424B-A47B-Base-PT": {
DownloadSource.DEFAULT: "baidu/ERNIE-4.5-VL-424B-A47B-PT",
DownloadSource.MODELSCOPE: "PaddlePaddle/ERNIE-4.5-VL-424B-A47B-PT",
},
},
template="ernie_vl",
multimodal=True,
)
register_model_group(
models={
"EXAONE-3.0-7.8B-Instruct": {
DownloadSource.DEFAULT: "LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct",
},
},
template="exaone",
)
register_model_group(
models={
"Falcon-7B": {
DownloadSource.DEFAULT: "tiiuae/falcon-7b",
DownloadSource.MODELSCOPE: "AI-ModelScope/falcon-7b",
},
"Falcon-11B": {
DownloadSource.DEFAULT: "tiiuae/falcon-11B",
DownloadSource.MODELSCOPE: "tiiuae/falcon-11B",
},
"Falcon-40B": {
DownloadSource.DEFAULT: "tiiuae/falcon-40b",
DownloadSource.MODELSCOPE: "AI-ModelScope/falcon-40b",
},
"Falcon-180B": {
DownloadSource.DEFAULT: "tiiuae/falcon-180b",
DownloadSource.MODELSCOPE: "modelscope/falcon-180B",
},
"Falcon-7B-Instruct": {
DownloadSource.DEFAULT: "tiiuae/falcon-7b-instruct",
DownloadSource.MODELSCOPE: "AI-ModelScope/falcon-7b-instruct",
},
"Falcon-40B-Instruct": {
DownloadSource.DEFAULT: "tiiuae/falcon-40b-instruct",
DownloadSource.MODELSCOPE: "AI-ModelScope/falcon-40b-instruct",
},
"Falcon-180B-Chat": {
DownloadSource.DEFAULT: "tiiuae/falcon-180b-chat",
DownloadSource.MODELSCOPE: "modelscope/falcon-180B-chat",
},
},
template="falcon",
)
register_model_group(
models={
"Falcon-H1-0.5B-Base": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-0.5B-Base",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-0.5B-Base",
},
"Falcon-H1-1.5B-Base": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-1.5B-Base",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Base",
},
"Falcon-H1-1.5B-Deep-Base": {
DownloadSource.DEFAULT: "tiuae/Falcon-H1-1.5B-Deep-Base",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Deep-Base",
},
"Falcon-H1-3B-Base": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-3B-Base",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-3B-Base",
},
"Falcon-H1-7B-Base": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-7B-Base",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-7B-Base",
},
"Falcon-H1-34B-Base": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-34B-Base",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-34B-Base",
},
"Falcon-H1-0.5B-Instruct": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-0.5B-Instruct",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-0.5B-Instruct",
},
"Falcon-H1-1.5B-Instruct": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-1.5B-Instruct",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Instruct",
},
"Falcon-H1-1.5B-Deep-Instruct": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-1.5B-Deep-Instruct",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-1.5B-Deep-Instruct",
},
"Falcon-H1-3B-Instruct": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-3B-Instruct",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-3B-Instruct",
},
"Falcon-H1-7B-Instruct": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-7B-Instruct",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-7B-Instruct",
},
"Falcon-H1-34B-Instruct": {
DownloadSource.DEFAULT: "tiiuae/Falcon-H1-34B-Instruct",
DownloadSource.MODELSCOPE: "tiiuae/Falcon-H1-34B-Instruct",
},
},
template="falcon_h1",
)
register_model_group(
models={
"Gemma-2B": {
DownloadSource.DEFAULT: "google/gemma-2b",
DownloadSource.MODELSCOPE: "AI-ModelScope/gemma-2b",
},
"Gemma-7B": {
DownloadSource.DEFAULT: "google/gemma-7b",
DownloadSource.MODELSCOPE: "AI-ModelScope/gemma-2b-it",
},
"Gemma-2B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-2b-it",
DownloadSource.MODELSCOPE: "AI-ModelScope/gemma-7b",
},
"Gemma-7B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-7b-it",
DownloadSource.MODELSCOPE: "AI-ModelScope/gemma-7b-it",
},
"Gemma-1.1-2B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-1.1-2b-it",
},
"Gemma-1.1-7B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-1.1-7b-it",
},
},
template="gemma",
)
register_model_group(
models={
"Gemma-2-2B": {
DownloadSource.DEFAULT: "google/gemma-2-2b",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-2b",
},
"Gemma-2-9B": {
DownloadSource.DEFAULT: "google/gemma-2-9b",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-9b",
},
"Gemma-2-27B": {
DownloadSource.DEFAULT: "google/gemma-2-27b",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-27b",
},
"Gemma-2-2B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-2-2b-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-2b-it",
DownloadSource.OPENMIND: "LlamaFactory/gemma-2-2b-it",
},
"Gemma-2-9B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-2-9b-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-9b-it",
DownloadSource.OPENMIND: "LlamaFactory/gemma-2-9b-it",
},
"Gemma-2-27B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-2-27b-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-2-27b-it",
},
"Gemma-3-270M": {
DownloadSource.DEFAULT: "google/gemma-3-270m",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-270m",
},
"Gemma-3-1B": {
DownloadSource.DEFAULT: "google/gemma-3-1b-pt",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-1b-pt",
},
"Gemma-3-270M-Instruct": {
DownloadSource.DEFAULT: "google/gemma-3-270m-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-270m-it",
},
"Gemma-3-1B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-3-1b-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-1b-it",
},
"MedGemma-27B-Instruct": {
DownloadSource.DEFAULT: "google/medgemma-27b-text-it",
DownloadSource.MODELSCOPE: "google/medgemma-27b-text-it",
},
},
template="gemma2",
)
register_model_group(
models={
"Gemma-3-4B": {
DownloadSource.DEFAULT: "google/gemma-3-4b-pt",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-4b-pt",
},
"Gemma-3-12B": {
DownloadSource.DEFAULT: "google/gemma-3-12b-pt",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-12b-pt",
},
"Gemma-3-27B": {
DownloadSource.DEFAULT: "google/gemma-3-27b-pt",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-27b-pt",
},
"Gemma-3-4B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-3-4b-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-4b-it",
},
"Gemma-3-12B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-3-12b-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-12b-it",
},
"Gemma-3-27B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-3-27b-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3-27b-it",
},
"MedGemma-4B": {
DownloadSource.DEFAULT: "google/medgemma-4b-pt",
DownloadSource.MODELSCOPE: "google/medgemma-4b-pt",
},
"MedGemma-4B-Instruct": {
DownloadSource.DEFAULT: "google/medgemma-4b-it",
DownloadSource.MODELSCOPE: "google/medgemma-4b-it",
},
"MedGemma-27B-Instruct": {
DownloadSource.DEFAULT: "google/medgemma-27b-text-it",
DownloadSource.MODELSCOPE: "google/medgemma-27b-text-it",
},
},
template="gemma3",
multimodal=True,
)
register_model_group(
models={
"Gemma-3n-E2B": {
DownloadSource.DEFAULT: "google/gemma-3n-E2B",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E2B",
},
"Gemma-3n-E4B": {
DownloadSource.DEFAULT: "google/gemma-3n-E4B",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E4B",
},
"Gemma-3n-E2B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-3n-E2B-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E2B-it",
},
"Gemma-3n-E4B-Instruct": {
DownloadSource.DEFAULT: "google/gemma-3n-E4B-it",
DownloadSource.MODELSCOPE: "LLM-Research/gemma-3n-E4B-it",
},
},
template="gemma3n",
multimodal=True,
)
register_model_group(
models={
"GLM-4-9B": {
DownloadSource.DEFAULT: "zai-org/glm-4-9b",
DownloadSource.MODELSCOPE: "ZhipuAI/glm-4-9b",
},
"GLM-4-9B-Chat": {
DownloadSource.DEFAULT: "zai-org/glm-4-9b-chat",
DownloadSource.MODELSCOPE: "ZhipuAI/glm-4-9b-chat",
DownloadSource.OPENMIND: "LlamaFactory/glm-4-9b-chat",
},
"GLM-4-9B-1M-Chat": {
DownloadSource.DEFAULT: "zai-org/glm-4-9b-chat-1m",
DownloadSource.MODELSCOPE: "ZhipuAI/glm-4-9b-chat-1m",
},
"GLM-4-0414-9B-Chat": {
DownloadSource.DEFAULT: "zai-org/GLM-4-9B-0414",
DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4-9B-0414",
},
"GLM-4-0414-32B-Base": {
DownloadSource.DEFAULT: "zai-org/GLM-4-32B-Base-0414",
DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4-32B-Base-0414",
},
"GLM-4-0414-32B-Chat": {
DownloadSource.DEFAULT: "zai-org/GLM-4-32B-0414",
DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4-32B-0414",
},
},
template="glm4",
)
register_model_group(
models={
"GLM-4.1V-9B-Base": {
DownloadSource.DEFAULT: "zai-org/GLM-4.1V-9B-Base",
DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4.1V-9B-Base",
},
"GLM-4.1V-9B-Thinking": {
DownloadSource.DEFAULT: "zai-org/GLM-4.1V-9B-Thinking",
DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4.1V-9B-Thinking",
},
},
template="glm4v",
multimodal=True,
)
register_model_group(
models={
"GLM-4.5-Air-Base": {
DownloadSource.DEFAULT: "zai-org/GLM-4.5-Air-Base",
DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4.5-Air-Base",
},
"GLM-4.5-Base": {
DownloadSource.DEFAULT: "zai-org/GLM-4.5-Base",
DownloadSource.MODELSCOPE: "ZhipuAI/GLM-4.5-Base",
},
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | true |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/extras/misc.py | src/llamafactory/extras/misc.py | # Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's PEFT library.
# https://github.com/huggingface/peft/blob/v0.10.0/src/peft/peft_model.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import gc
import os
import socket
from typing import TYPE_CHECKING, Any, Literal, Optional, Union
import torch
import torch.distributed as dist
import transformers.dynamic_module_utils
from huggingface_hub.utils import WeakFileLock
from transformers import InfNanRemoveLogitsProcessor, LogitsProcessorList
from transformers.dynamic_module_utils import get_relative_imports
from transformers.utils import (
is_torch_bf16_gpu_available,
is_torch_cuda_available,
is_torch_mps_available,
is_torch_npu_available,
is_torch_xpu_available,
)
from transformers.utils.versions import require_version
from . import logging
_is_fp16_available = is_torch_npu_available() or is_torch_cuda_available()
try:
_is_bf16_available = is_torch_bf16_gpu_available() or (is_torch_npu_available() and torch.npu.is_bf16_supported())
except Exception:
_is_bf16_available = False
if TYPE_CHECKING:
from numpy.typing import NDArray
from ..hparams import ModelArguments
logger = logging.get_logger(__name__)
class AverageMeter:
r"""Compute and store the average and current value."""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def check_version(requirement: str, mandatory: bool = False) -> None:
r"""Optionally check the package version."""
if is_env_enabled("DISABLE_VERSION_CHECK") and not mandatory:
logger.warning_rank0_once("Version checking has been disabled, may lead to unexpected behaviors.")
return
if "gptmodel" in requirement or "autoawq" in requirement:
pip_command = f"pip install {requirement} --no-build-isolation"
else:
pip_command = f"pip install {requirement}"
if mandatory:
hint = f"To fix: run `{pip_command}`."
else:
hint = f"To fix: run `{pip_command}` or set `DISABLE_VERSION_CHECK=1` to skip this check."
require_version(requirement, hint)
def check_dependencies() -> None:
r"""Check the version of the required packages."""
check_version("transformers>=4.51.0,<=4.57.1")
check_version("datasets>=2.16.0,<=4.0.0")
check_version("accelerate>=1.3.0,<=1.11.0")
check_version("peft>=0.14.0,<=0.17.1")
check_version("trl>=0.18.0,<=0.24.0")
def calculate_tps(dataset: list[dict[str, Any]], metrics: dict[str, float], stage: Literal["sft", "rm"]) -> float:
r"""Calculate effective tokens per second."""
effective_token_num = 0
for data in dataset:
if stage == "sft":
effective_token_num += len(data["input_ids"])
elif stage == "rm":
effective_token_num += len(data["chosen_input_ids"]) + len(data["rejected_input_ids"])
result = effective_token_num * metrics["epoch"] / metrics["train_runtime"]
return result / dist.get_world_size() if dist.is_initialized() else result
def count_parameters(model: "torch.nn.Module") -> tuple[int, int]:
r"""Return the number of trainable parameters and number of all parameters in the model."""
trainable_params, all_param = 0, 0
for param in model.parameters():
num_params = param.numel()
# if using DS Zero 3 and the weights are initialized empty
if num_params == 0 and hasattr(param, "ds_numel"):
num_params = param.ds_numel
# Due to the design of 4bit linear layers from bitsandbytes, multiply the number of parameters by itemsize
if param.__class__.__name__ == "Params4bit":
if hasattr(param, "quant_storage") and hasattr(param.quant_storage, "itemsize"):
num_bytes = param.quant_storage.itemsize
elif hasattr(param, "element_size"): # for older pytorch version
num_bytes = param.element_size()
else:
num_bytes = 1
num_params = num_params * 2 * num_bytes
all_param += num_params
if param.requires_grad:
trainable_params += num_params
return trainable_params, all_param
def get_current_device() -> "torch.device":
r"""Get the current available device."""
if is_torch_xpu_available():
device = "xpu:{}".format(os.getenv("LOCAL_RANK", "0"))
elif is_torch_npu_available():
device = "npu:{}".format(os.getenv("LOCAL_RANK", "0"))
elif is_torch_mps_available():
device = "mps:{}".format(os.getenv("LOCAL_RANK", "0"))
elif is_torch_cuda_available():
device = "cuda:{}".format(os.getenv("LOCAL_RANK", "0"))
else:
device = "cpu"
return torch.device(device)
def get_device_count() -> int:
r"""Get the number of available devices."""
if is_torch_xpu_available():
return torch.xpu.device_count()
elif is_torch_npu_available():
return torch.npu.device_count()
elif is_torch_mps_available():
return torch.mps.device_count()
elif is_torch_cuda_available():
return torch.cuda.device_count()
else:
return 0
def get_logits_processor() -> "LogitsProcessorList":
r"""Get logits processor that removes NaN and Inf logits."""
logits_processor = LogitsProcessorList()
logits_processor.append(InfNanRemoveLogitsProcessor())
return logits_processor
def get_current_memory() -> tuple[int, int]:
r"""Get the available and total memory for the current device (in Bytes)."""
if is_torch_xpu_available():
return torch.xpu.mem_get_info()
elif is_torch_npu_available():
return torch.npu.mem_get_info()
elif is_torch_mps_available():
return torch.mps.current_allocated_memory(), torch.mps.recommended_max_memory()
elif is_torch_cuda_available():
return torch.cuda.mem_get_info()
else:
return 0, -1
def get_peak_memory() -> tuple[int, int]:
r"""Get the peak memory usage (allocated, reserved) for the current device (in Bytes)."""
if is_torch_xpu_available():
return torch.xpu.max_memory_allocated(), torch.xpu.max_memory_reserved()
elif is_torch_npu_available():
return torch.npu.max_memory_allocated(), torch.npu.max_memory_reserved()
elif is_torch_mps_available():
return torch.mps.current_allocated_memory(), -1
elif is_torch_cuda_available():
return torch.cuda.max_memory_allocated(), torch.cuda.max_memory_reserved()
else:
return 0, -1
def has_tokenized_data(path: "os.PathLike") -> bool:
r"""Check if the path has a tokenized dataset."""
return os.path.isdir(path) and len(os.listdir(path)) > 0
def infer_optim_dtype(model_dtype: Optional["torch.dtype"]) -> "torch.dtype":
r"""Infer the optimal dtype according to the model_dtype and device compatibility."""
if _is_bf16_available and (model_dtype == torch.bfloat16 or model_dtype is None):
return torch.bfloat16
elif _is_fp16_available:
return torch.float16
else:
return torch.float32
def is_accelerator_available() -> bool:
r"""Check if the accelerator is available."""
return (
is_torch_xpu_available() or is_torch_npu_available() or is_torch_mps_available() or is_torch_cuda_available()
)
def is_env_enabled(env_var: str, default: str = "0") -> bool:
r"""Check if the environment variable is enabled."""
return os.getenv(env_var, default).lower() in ["true", "y", "1"]
def numpify(inputs: Union["NDArray", "torch.Tensor"]) -> "NDArray":
r"""Cast a torch tensor or a numpy array to a numpy array."""
if isinstance(inputs, torch.Tensor):
inputs = inputs.cpu()
if inputs.dtype == torch.bfloat16: # numpy does not support bfloat16 until 1.21.4
inputs = inputs.to(torch.float32)
inputs = inputs.numpy()
return inputs
def skip_check_imports() -> None:
r"""Avoid flash attention import error in custom model files."""
if not is_env_enabled("FORCE_CHECK_IMPORTS"):
transformers.dynamic_module_utils.check_imports = get_relative_imports
def torch_gc() -> None:
r"""Collect the device memory."""
gc.collect()
if is_torch_xpu_available():
torch.xpu.empty_cache()
elif is_torch_npu_available():
torch.npu.empty_cache()
elif is_torch_mps_available():
torch.mps.empty_cache()
elif is_torch_cuda_available():
torch.cuda.empty_cache()
def try_download_model_from_other_hub(model_args: "ModelArguments") -> str:
if (not use_modelscope() and not use_openmind()) or os.path.exists(model_args.model_name_or_path):
return model_args.model_name_or_path
if use_modelscope():
check_version("modelscope>=1.14.0", mandatory=True)
from modelscope import snapshot_download # type: ignore
from modelscope.hub.api import HubApi # type: ignore
if model_args.ms_hub_token:
api = HubApi()
api.login(model_args.ms_hub_token)
revision = "master" if model_args.model_revision == "main" else model_args.model_revision
with WeakFileLock(os.path.abspath(os.path.expanduser("~/.cache/llamafactory/modelscope.lock"))):
model_path = snapshot_download(
model_args.model_name_or_path,
revision=revision,
cache_dir=model_args.cache_dir,
)
return model_path
if use_openmind():
check_version("openmind>=0.8.0", mandatory=True)
from openmind.utils.hub import snapshot_download # type: ignore
with WeakFileLock(os.path.abspath(os.path.expanduser("~/.cache/llamafactory/openmind.lock"))):
model_path = snapshot_download(
model_args.model_name_or_path,
revision=model_args.model_revision,
cache_dir=model_args.cache_dir,
)
return model_path
def use_modelscope() -> bool:
return is_env_enabled("USE_MODELSCOPE_HUB")
def use_openmind() -> bool:
return is_env_enabled("USE_OPENMIND_HUB")
def use_ray() -> bool:
return is_env_enabled("USE_RAY")
def use_kt() -> bool:
return is_env_enabled("USE_KT")
def find_available_port() -> int:
r"""Find an available port on the local machine."""
sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
sock.bind(("", 0))
port = sock.getsockname()[1]
sock.close()
return port
def fix_proxy(ipv6_enabled: bool = False) -> None:
r"""Fix proxy settings for gradio ui."""
os.environ["no_proxy"] = "localhost,127.0.0.1,0.0.0.0"
if ipv6_enabled:
os.environ.pop("http_proxy", None)
os.environ.pop("HTTP_PROXY", None)
os.environ.pop("https_proxy", None)
os.environ.pop("HTTPS_PROXY", None)
os.environ.pop("all_proxy", None)
os.environ.pop("ALL_PROXY", None)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/extras/logging.py | src/llamafactory/extras/logging.py | # Copyright 2025 Optuna, HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/utils/logging.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
import sys
import threading
from concurrent.futures import ThreadPoolExecutor
from functools import lru_cache
from typing import Optional
from .constants import RUNNING_LOG
_thread_lock = threading.RLock()
_default_handler: Optional["logging.Handler"] = None
_default_log_level: "logging._Level" = logging.INFO
class LoggerHandler(logging.Handler):
r"""Redirect the logging output to the logging file for LLaMA Board."""
def __init__(self, output_dir: str) -> None:
super().__init__()
self._formatter = logging.Formatter(
fmt="[%(levelname)s|%(asctime)s] %(filename)s:%(lineno)s >> %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
self.setLevel(logging.INFO)
os.makedirs(output_dir, exist_ok=True)
self.running_log = os.path.join(output_dir, RUNNING_LOG)
if os.path.exists(self.running_log):
os.remove(self.running_log)
self.thread_pool = ThreadPoolExecutor(max_workers=1)
def _write_log(self, log_entry: str) -> None:
with open(self.running_log, "a", encoding="utf-8") as f:
f.write(log_entry + "\n")
def emit(self, record) -> None:
if record.name == "httpx":
return
log_entry = self._formatter.format(record)
self.thread_pool.submit(self._write_log, log_entry)
def close(self) -> None:
self.thread_pool.shutdown(wait=True)
return super().close()
class _Logger(logging.Logger):
r"""A logger that supports rank0 logging."""
def info_rank0(self, *args, **kwargs) -> None:
self.info(*args, **kwargs)
def warning_rank0(self, *args, **kwargs) -> None:
self.warning(*args, **kwargs)
def warning_rank0_once(self, *args, **kwargs) -> None:
self.warning(*args, **kwargs)
def _get_default_logging_level() -> "logging._Level":
r"""Return the default logging level."""
env_level_str = os.getenv("LLAMAFACTORY_VERBOSITY", None)
if env_level_str:
if env_level_str.upper() in logging._nameToLevel:
return logging._nameToLevel[env_level_str.upper()]
else:
raise ValueError(f"Unknown logging level: {env_level_str}.")
return _default_log_level
def _get_library_name() -> str:
return __name__.split(".")[0]
def _get_library_root_logger() -> "_Logger":
return logging.getLogger(_get_library_name())
def _configure_library_root_logger() -> None:
r"""Configure root logger using a stdout stream handler with an explicit format."""
global _default_handler
with _thread_lock:
if _default_handler: # already configured
return
formatter = logging.Formatter(
fmt="[%(levelname)s|%(asctime)s] %(name)s:%(lineno)s >> %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
_default_handler = logging.StreamHandler(sys.stdout)
_default_handler.setFormatter(formatter)
library_root_logger = _get_library_root_logger()
library_root_logger.addHandler(_default_handler)
library_root_logger.setLevel(_get_default_logging_level())
library_root_logger.propagate = False
def get_logger(name: str | None = None) -> "_Logger":
r"""Return a logger with the specified name. It it not supposed to be accessed externally."""
if name is None:
name = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(name)
def add_handler(handler: "logging.Handler") -> None:
r"""Add a handler to the root logger."""
_configure_library_root_logger()
_get_library_root_logger().addHandler(handler)
def remove_handler(handler: logging.Handler) -> None:
r"""Remove a handler to the root logger."""
_configure_library_root_logger()
_get_library_root_logger().removeHandler(handler)
def info_rank0(self: "logging.Logger", *args, **kwargs) -> None:
if int(os.getenv("LOCAL_RANK", "0")) == 0:
self.info(*args, **kwargs)
def warning_rank0(self: "logging.Logger", *args, **kwargs) -> None:
if int(os.getenv("LOCAL_RANK", "0")) == 0:
self.warning(*args, **kwargs)
@lru_cache(None)
def warning_rank0_once(self: "logging.Logger", *args, **kwargs) -> None:
if int(os.getenv("LOCAL_RANK", "0")) == 0:
self.warning(*args, **kwargs)
logging.Logger.info_rank0 = info_rank0
logging.Logger.warning_rank0 = warning_rank0
logging.Logger.warning_rank0_once = warning_rank0_once
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/extras/__init__.py | src/llamafactory/extras/__init__.py | python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false | |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/extras/ploting.py | src/llamafactory/extras/ploting.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import math
import os
from typing import Any
from transformers.trainer import TRAINER_STATE_NAME
from . import logging
from .packages import is_matplotlib_available
if is_matplotlib_available():
import matplotlib.figure
import matplotlib.pyplot as plt
logger = logging.get_logger(__name__)
def smooth(scalars: list[float]) -> list[float]:
r"""EMA implementation according to TensorBoard."""
if len(scalars) == 0:
return []
last = scalars[0]
smoothed = []
weight = 1.8 * (1 / (1 + math.exp(-0.05 * len(scalars))) - 0.5) # a sigmoid function
for next_val in scalars:
smoothed_val = last * weight + (1 - weight) * next_val
smoothed.append(smoothed_val)
last = smoothed_val
return smoothed
def gen_loss_plot(trainer_log: list[dict[str, Any]]) -> "matplotlib.figure.Figure":
r"""Plot loss curves in LlamaBoard."""
plt.close("all")
plt.switch_backend("agg")
fig = plt.figure()
ax = fig.add_subplot(111)
steps, losses = [], []
for log in trainer_log:
if log.get("loss", None):
steps.append(log["current_steps"])
losses.append(log["loss"])
ax.plot(steps, losses, color="#1f77b4", alpha=0.4, label="original")
ax.plot(steps, smooth(losses), color="#1f77b4", label="smoothed")
ax.legend()
ax.set_xlabel("step")
ax.set_ylabel("loss")
return fig
def plot_loss(save_dictionary: str, keys: list[str] = ["loss"]) -> None:
r"""Plot loss curves and saves the image."""
plt.switch_backend("agg")
with open(os.path.join(save_dictionary, TRAINER_STATE_NAME), encoding="utf-8") as f:
data = json.load(f)
for key in keys:
steps, metrics = [], []
for i in range(len(data["log_history"])):
if key in data["log_history"][i]:
steps.append(data["log_history"][i]["step"])
metrics.append(data["log_history"][i][key])
if len(metrics) == 0:
logger.warning_rank0(f"No metric {key} to plot.")
continue
plt.figure()
plt.plot(steps, metrics, color="#1f77b4", alpha=0.4, label="original")
plt.plot(steps, smooth(metrics), color="#1f77b4", label="smoothed")
plt.title(f"training {key} of {save_dictionary}")
plt.xlabel("step")
plt.ylabel(key)
plt.legend()
figure_path = os.path.join(save_dictionary, "training_{}.png".format(key.replace("/", "_")))
plt.savefig(figure_path, format="png", dpi=100)
print("Figure saved at:", figure_path)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/extras/env.py | src/llamafactory/extras/env.py | # Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/commands/env.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import OrderedDict
VERSION = "0.9.5.dev0"
def print_env() -> None:
import os
import platform
import accelerate
import datasets
import peft
import torch
import transformers
from transformers.utils import is_torch_cuda_available, is_torch_npu_available
info = OrderedDict(
{
"`llamafactory` version": VERSION,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"PyTorch version": torch.__version__,
"Transformers version": transformers.__version__,
"Datasets version": datasets.__version__,
"Accelerate version": accelerate.__version__,
"PEFT version": peft.__version__,
}
)
if is_torch_cuda_available():
info["PyTorch version"] += " (GPU)"
info["GPU type"] = torch.cuda.get_device_name()
info["GPU number"] = torch.cuda.device_count()
info["GPU memory"] = f"{torch.cuda.mem_get_info()[1] / (1024**3):.2f}GB"
if is_torch_npu_available():
info["PyTorch version"] += " (NPU)"
info["NPU type"] = torch.npu.get_device_name()
info["CANN version"] = torch.version.cann
try:
import trl # type: ignore
info["TRL version"] = trl.__version__
except Exception:
pass
try:
import deepspeed # type: ignore
info["DeepSpeed version"] = deepspeed.__version__
except Exception:
pass
try:
import bitsandbytes # type: ignore
info["Bitsandbytes version"] = bitsandbytes.__version__
except Exception:
pass
try:
import vllm
info["vLLM version"] = vllm.__version__
except Exception:
pass
try:
import subprocess
commit_info = subprocess.run(["git", "rev-parse", "HEAD"], capture_output=True, text=True, check=True)
commit_hash = commit_info.stdout.strip()
info["Git commit"] = commit_hash
except Exception:
pass
if os.path.exists("data"):
info["Default data directory"] = "detected"
else:
info["Default data directory"] = "not detected"
print("\n" + "\n".join([f"- {key}: {value}" for key, value in info.items()]) + "\n")
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/extras/packages.py | src/llamafactory/extras/packages.py | # Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/utils/import_utils.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import importlib.metadata
import importlib.util
from functools import lru_cache
from typing import TYPE_CHECKING
from packaging import version
if TYPE_CHECKING:
from packaging.version import Version
def _is_package_available(name: str) -> bool:
return importlib.util.find_spec(name) is not None
def _get_package_version(name: str) -> "Version":
try:
return version.parse(importlib.metadata.version(name))
except Exception:
return version.parse("0.0.0")
def is_pyav_available():
return _is_package_available("av")
def is_librosa_available():
return _is_package_available("librosa")
def is_fastapi_available():
return _is_package_available("fastapi")
def is_galore_available():
return _is_package_available("galore_torch")
def is_apollo_available():
return _is_package_available("apollo_torch")
def is_jieba_available():
return _is_package_available("jieba")
def is_gradio_available():
return _is_package_available("gradio")
def is_matplotlib_available():
return _is_package_available("matplotlib")
def is_mcore_adapter_available():
return _is_package_available("mcore_adapter")
def is_pillow_available():
return _is_package_available("PIL")
def is_ray_available():
return _is_package_available("ray")
def is_kt_available():
return _is_package_available("ktransformers")
def is_requests_available():
return _is_package_available("requests")
def is_rouge_available():
return _is_package_available("rouge_chinese")
def is_safetensors_available():
return _is_package_available("safetensors")
def is_sglang_available():
return _is_package_available("sglang")
def is_starlette_available():
return _is_package_available("sse_starlette")
@lru_cache
def is_transformers_version_greater_than(content: str):
return _get_package_version("transformers") >= version.parse(content)
@lru_cache
def is_torch_version_greater_than(content: str):
return _get_package_version("torch") >= version.parse(content)
def is_uvicorn_available():
return _is_package_available("uvicorn")
def is_vllm_available():
return _is_package_available("vllm")
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/chatter.py | src/llamafactory/webui/chatter.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from collections.abc import Generator
from contextlib import contextmanager
from typing import TYPE_CHECKING, Any
from transformers.utils import is_torch_npu_available
from ..chat import ChatModel
from ..data import Role
from ..extras.constants import PEFT_METHODS
from ..extras.misc import torch_gc
from ..extras.packages import is_gradio_available
from .common import get_save_dir, load_config
from .locales import ALERTS
if TYPE_CHECKING:
from ..chat import BaseEngine
from .manager import Manager
if is_gradio_available():
import gradio as gr
def _escape_html(text: str) -> str:
r"""Escape HTML characters."""
return text.replace("<", "<").replace(">", ">")
def _format_response(text: str, lang: str, escape_html: bool, thought_words: tuple[str, str]) -> str:
r"""Post-process the response text.
Based on: https://huggingface.co/spaces/Lyte/DeepSeek-R1-Distill-Qwen-1.5B-Demo-GGUF/blob/main/app.py
"""
if thought_words[0] not in text:
return _escape_html(text) if escape_html else text
text = text.replace(thought_words[0], "")
result = text.split(thought_words[1], maxsplit=1)
if len(result) == 1:
summary = ALERTS["info_thinking"][lang]
thought, answer = text, ""
else:
summary = ALERTS["info_thought"][lang]
thought, answer = result
if escape_html:
thought, answer = _escape_html(thought), _escape_html(answer)
return (
f"<details open><summary class='thinking-summary'><span>{summary}</span></summary>\n\n"
f"<div class='thinking-container'>\n{thought}\n</div>\n</details>{answer}"
)
@contextmanager
def update_attr(obj: Any, name: str, value: Any):
old_value = getattr(obj, name, None)
setattr(obj, name, value)
yield
setattr(obj, name, old_value)
class WebChatModel(ChatModel):
def __init__(self, manager: "Manager", demo_mode: bool = False, lazy_init: bool = True) -> None:
self.manager = manager
self.demo_mode = demo_mode
self.engine: BaseEngine | None = None
if not lazy_init: # read arguments from command line
super().__init__()
if demo_mode and os.getenv("DEMO_MODEL") and os.getenv("DEMO_TEMPLATE"): # load demo model
model_name_or_path = os.getenv("DEMO_MODEL")
template = os.getenv("DEMO_TEMPLATE")
infer_backend = os.getenv("DEMO_BACKEND", "huggingface")
super().__init__(
dict(model_name_or_path=model_name_or_path, template=template, infer_backend=infer_backend)
)
@property
def loaded(self) -> bool:
return self.engine is not None
def load_model(self, data) -> Generator[str, None, None]:
get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
lang, model_name, model_path = get("top.lang"), get("top.model_name"), get("top.model_path")
finetuning_type, checkpoint_path = get("top.finetuning_type"), get("top.checkpoint_path")
user_config = load_config()
error = ""
if self.loaded:
error = ALERTS["err_exists"][lang]
elif not model_name:
error = ALERTS["err_no_model"][lang]
elif not model_path:
error = ALERTS["err_no_path"][lang]
elif self.demo_mode:
error = ALERTS["err_demo"][lang]
try:
json.loads(get("infer.extra_args"))
except json.JSONDecodeError:
error = ALERTS["err_json_schema"][lang]
if error:
gr.Warning(error)
yield error
return
yield ALERTS["info_loading"][lang]
args = dict(
model_name_or_path=model_path,
cache_dir=user_config.get("cache_dir", None),
finetuning_type=finetuning_type,
template=get("top.template"),
rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") != "none" else None,
flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
use_unsloth=(get("top.booster") == "unsloth"),
enable_liger_kernel=(get("top.booster") == "liger_kernel"),
infer_backend=get("infer.infer_backend"),
infer_dtype=get("infer.infer_dtype"),
trust_remote_code=True,
)
args.update(json.loads(get("infer.extra_args")))
# checkpoints
if checkpoint_path:
if finetuning_type in PEFT_METHODS: # list
args["adapter_name_or_path"] = ",".join(
[get_save_dir(model_name, finetuning_type, adapter) for adapter in checkpoint_path]
)
else: # str
args["model_name_or_path"] = get_save_dir(model_name, finetuning_type, checkpoint_path)
# quantization
if get("top.quantization_bit") != "none":
args["quantization_bit"] = int(get("top.quantization_bit"))
args["quantization_method"] = get("top.quantization_method")
args["double_quantization"] = not is_torch_npu_available()
super().__init__(args)
yield ALERTS["info_loaded"][lang]
def unload_model(self, data) -> Generator[str, None, None]:
lang = data[self.manager.get_elem_by_id("top.lang")]
if self.demo_mode:
gr.Warning(ALERTS["err_demo"][lang])
yield ALERTS["err_demo"][lang]
return
yield ALERTS["info_unloading"][lang]
self.engine = None
torch_gc()
yield ALERTS["info_unloaded"][lang]
@staticmethod
def append(
chatbot: list[dict[str, str]],
messages: list[dict[str, str]],
role: str,
query: str,
escape_html: bool,
) -> tuple[list[dict[str, str]], list[dict[str, str]], str]:
r"""Add the user input to chatbot.
Inputs: infer.chatbot, infer.messages, infer.role, infer.query, infer.escape_html
Output: infer.chatbot, infer.messages, infer.query
"""
return (
chatbot + [{"role": "user", "content": _escape_html(query) if escape_html else query}],
messages + [{"role": role, "content": query}],
"",
)
def stream(
self,
chatbot: list[dict[str, str]],
messages: list[dict[str, str]],
lang: str,
system: str,
tools: str,
image: Any | None,
video: Any | None,
audio: Any | None,
max_new_tokens: int,
top_p: float,
temperature: float,
skip_special_tokens: bool,
escape_html: bool,
enable_thinking: bool,
) -> Generator[tuple[list[dict[str, str]], list[dict[str, str]]], None, None]:
r"""Generate output text in stream.
Inputs: infer.chatbot, infer.messages, infer.system, infer.tools, infer.image, infer.video, ...
Output: infer.chatbot, infer.messages
"""
with update_attr(self.engine.template, "enable_thinking", enable_thinking):
chatbot.append({"role": "assistant", "content": ""})
response = ""
for new_text in self.stream_chat(
messages,
system,
tools,
images=[image] if image else None,
videos=[video] if video else None,
audios=[audio] if audio else None,
max_new_tokens=max_new_tokens,
top_p=top_p,
temperature=temperature,
skip_special_tokens=skip_special_tokens,
):
response += new_text
if tools:
result = self.engine.template.extract_tool(response)
else:
result = response
if isinstance(result, list):
tool_calls = [{"name": tool.name, "arguments": json.loads(tool.arguments)} for tool in result]
tool_calls = json.dumps(tool_calls, ensure_ascii=False)
output_messages = messages + [{"role": Role.FUNCTION.value, "content": tool_calls}]
bot_text = "```json\n" + tool_calls + "\n```"
else:
output_messages = messages + [{"role": Role.ASSISTANT.value, "content": result}]
bot_text = _format_response(result, lang, escape_html, self.engine.template.thought_words)
chatbot[-1] = {"role": "assistant", "content": bot_text}
yield chatbot, output_messages
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/runner.py | src/llamafactory/webui/runner.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from collections.abc import Generator
from copy import deepcopy
from subprocess import PIPE, Popen, TimeoutExpired
from typing import TYPE_CHECKING, Any
from transformers.utils import is_torch_npu_available
from ..extras.constants import LLAMABOARD_CONFIG, MULTIMODAL_SUPPORTED_MODELS, PEFT_METHODS, TRAINING_STAGES
from ..extras.misc import is_accelerator_available, torch_gc
from ..extras.packages import is_gradio_available
from .common import (
DEFAULT_CACHE_DIR,
DEFAULT_CONFIG_DIR,
abort_process,
calculate_pixels,
gen_cmd,
get_save_dir,
load_args,
load_config,
load_eval_results,
save_args,
save_cmd,
)
from .control import get_trainer_info
from .locales import ALERTS, LOCALES
if is_gradio_available():
import gradio as gr
if TYPE_CHECKING:
from gradio.components import Component
from .manager import Manager
class Runner:
r"""A class to manage the running status of the trainers."""
def __init__(self, manager: "Manager", demo_mode: bool = False) -> None:
r"""Init a runner."""
self.manager = manager
self.demo_mode = demo_mode
""" Resume """
self.trainer: Popen | None = None
self.do_train = True
self.running_data: dict[Component, Any] = None
""" State """
self.aborted = False
self.running = False
def set_abort(self) -> None:
self.aborted = True
if self.trainer is not None:
abort_process(self.trainer.pid)
def _initialize(self, data: dict["Component", Any], do_train: bool, from_preview: bool) -> str:
r"""Validate the configuration."""
get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
lang, model_name, model_path = get("top.lang"), get("top.model_name"), get("top.model_path")
dataset = get("train.dataset") if do_train else get("eval.dataset")
if self.running:
return ALERTS["err_conflict"][lang]
if not model_name:
return ALERTS["err_no_model"][lang]
if not model_path:
return ALERTS["err_no_path"][lang]
if not dataset:
return ALERTS["err_no_dataset"][lang]
if not from_preview and self.demo_mode:
return ALERTS["err_demo"][lang]
if do_train:
if not get("train.output_dir"):
return ALERTS["err_no_output_dir"][lang]
try:
json.loads(get("train.extra_args"))
except json.JSONDecodeError:
return ALERTS["err_json_schema"][lang]
stage = TRAINING_STAGES[get("train.training_stage")]
if stage == "ppo" and not get("train.reward_model"):
return ALERTS["err_no_reward_model"][lang]
else:
if not get("eval.output_dir"):
return ALERTS["err_no_output_dir"][lang]
if not from_preview and not is_accelerator_available():
gr.Warning(ALERTS["warn_no_cuda"][lang])
return ""
def _finalize(self, lang: str, finish_info: str) -> None:
r"""Clean the cached memory and resets the runner."""
finish_info = ALERTS["info_aborted"][lang] if self.aborted else finish_info
gr.Info(finish_info)
self.trainer = None
self.aborted = False
self.running = False
self.running_data = None
torch_gc()
def _parse_train_args(self, data: dict["Component", Any]) -> dict[str, Any]:
r"""Build and validate the training arguments."""
get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
model_name, finetuning_type = get("top.model_name"), get("top.finetuning_type")
user_config = load_config()
args = dict(
stage=TRAINING_STAGES[get("train.training_stage")],
do_train=True,
model_name_or_path=get("top.model_path"),
cache_dir=user_config.get("cache_dir", None),
preprocessing_num_workers=16,
finetuning_type=finetuning_type,
template=get("top.template"),
rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") != "none" else None,
flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
use_unsloth=(get("top.booster") == "unsloth"),
enable_liger_kernel=(get("top.booster") == "liger_kernel"),
dataset_dir=get("train.dataset_dir"),
dataset=",".join(get("train.dataset")),
cutoff_len=get("train.cutoff_len"),
learning_rate=float(get("train.learning_rate")),
num_train_epochs=float(get("train.num_train_epochs")),
max_samples=int(get("train.max_samples")),
per_device_train_batch_size=get("train.batch_size"),
gradient_accumulation_steps=get("train.gradient_accumulation_steps"),
lr_scheduler_type=get("train.lr_scheduler_type"),
max_grad_norm=float(get("train.max_grad_norm")),
logging_steps=get("train.logging_steps"),
save_steps=get("train.save_steps"),
warmup_steps=get("train.warmup_steps"),
neftune_noise_alpha=get("train.neftune_alpha") or None,
packing=get("train.packing") or get("train.neat_packing"),
neat_packing=get("train.neat_packing"),
train_on_prompt=get("train.train_on_prompt"),
mask_history=get("train.mask_history"),
resize_vocab=get("train.resize_vocab"),
use_llama_pro=get("train.use_llama_pro"),
enable_thinking=get("train.enable_thinking"),
report_to=get("train.report_to"),
use_galore=get("train.use_galore"),
use_apollo=get("train.use_apollo"),
use_badam=get("train.use_badam"),
use_swanlab=get("train.use_swanlab"),
output_dir=get_save_dir(model_name, finetuning_type, get("train.output_dir")),
fp16=(get("train.compute_type") == "fp16"),
bf16=(get("train.compute_type") == "bf16"),
pure_bf16=(get("train.compute_type") == "pure_bf16"),
plot_loss=True,
trust_remote_code=True,
ddp_timeout=180000000,
include_num_input_tokens_seen=True,
)
args.update(json.loads(get("train.extra_args")))
# checkpoints
if get("top.checkpoint_path"):
if finetuning_type in PEFT_METHODS: # list
args["adapter_name_or_path"] = ",".join(
[get_save_dir(model_name, finetuning_type, adapter) for adapter in get("top.checkpoint_path")]
)
else: # str
args["model_name_or_path"] = get_save_dir(model_name, finetuning_type, get("top.checkpoint_path"))
# quantization
if get("top.quantization_bit") != "none":
args["quantization_bit"] = int(get("top.quantization_bit"))
args["quantization_method"] = get("top.quantization_method")
args["double_quantization"] = not is_torch_npu_available()
# freeze config
if args["finetuning_type"] == "freeze":
args["freeze_trainable_layers"] = get("train.freeze_trainable_layers")
args["freeze_trainable_modules"] = get("train.freeze_trainable_modules")
args["freeze_extra_modules"] = get("train.freeze_extra_modules") or None
# lora config
if args["finetuning_type"] == "lora":
args["lora_rank"] = get("train.lora_rank")
args["lora_alpha"] = get("train.lora_alpha")
args["lora_dropout"] = get("train.lora_dropout")
args["loraplus_lr_ratio"] = get("train.loraplus_lr_ratio") or None
args["create_new_adapter"] = get("train.create_new_adapter")
args["use_rslora"] = get("train.use_rslora")
args["use_dora"] = get("train.use_dora")
args["pissa_init"] = get("train.use_pissa")
args["pissa_convert"] = get("train.use_pissa")
args["lora_target"] = get("train.lora_target") or "all"
args["additional_target"] = get("train.additional_target") or None
if args["use_llama_pro"]:
args["freeze_trainable_layers"] = get("train.freeze_trainable_layers")
# rlhf config
if args["stage"] == "ppo":
if finetuning_type in PEFT_METHODS:
args["reward_model"] = ",".join(
[get_save_dir(model_name, finetuning_type, adapter) for adapter in get("train.reward_model")]
)
else:
args["reward_model"] = get_save_dir(model_name, finetuning_type, get("train.reward_model"))
args["reward_model_type"] = "lora" if finetuning_type == "lora" else "full"
args["ppo_score_norm"] = get("train.ppo_score_norm")
args["ppo_whiten_rewards"] = get("train.ppo_whiten_rewards")
args["top_k"] = 0
args["top_p"] = 0.9
elif args["stage"] in ["dpo", "kto"]:
args["pref_beta"] = get("train.pref_beta")
args["pref_ftx"] = get("train.pref_ftx")
args["pref_loss"] = get("train.pref_loss")
# multimodal config
if model_name in MULTIMODAL_SUPPORTED_MODELS:
args["freeze_vision_tower"] = get("train.freeze_vision_tower")
args["freeze_multi_modal_projector"] = get("train.freeze_multi_modal_projector")
args["freeze_language_model"] = get("train.freeze_language_model")
args["image_max_pixels"] = calculate_pixels(get("train.image_max_pixels"))
args["image_min_pixels"] = calculate_pixels(get("train.image_min_pixels"))
args["video_max_pixels"] = calculate_pixels(get("train.video_max_pixels"))
args["video_min_pixels"] = calculate_pixels(get("train.video_min_pixels"))
# galore config
if args["use_galore"]:
args["galore_rank"] = get("train.galore_rank")
args["galore_update_interval"] = get("train.galore_update_interval")
args["galore_scale"] = get("train.galore_scale")
args["galore_target"] = get("train.galore_target")
# apollo config
if args["use_apollo"]:
args["apollo_rank"] = get("train.apollo_rank")
args["apollo_update_interval"] = get("train.apollo_update_interval")
args["apollo_scale"] = get("train.apollo_scale")
args["apollo_target"] = get("train.apollo_target")
# badam config
if args["use_badam"]:
args["badam_mode"] = get("train.badam_mode")
args["badam_switch_mode"] = get("train.badam_switch_mode")
args["badam_switch_interval"] = get("train.badam_switch_interval")
args["badam_update_ratio"] = get("train.badam_update_ratio")
# swanlab config
if get("train.use_swanlab"):
args["swanlab_project"] = get("train.swanlab_project")
args["swanlab_run_name"] = get("train.swanlab_run_name")
args["swanlab_workspace"] = get("train.swanlab_workspace")
args["swanlab_api_key"] = get("train.swanlab_api_key")
args["swanlab_mode"] = get("train.swanlab_mode")
# eval config
if get("train.val_size") > 1e-6 and args["stage"] != "ppo":
args["val_size"] = get("train.val_size")
args["eval_strategy"] = "steps"
args["eval_steps"] = args["save_steps"]
args["per_device_eval_batch_size"] = args["per_device_train_batch_size"]
# ds config
if get("train.ds_stage") != "none":
ds_stage = get("train.ds_stage")
ds_offload = "offload_" if get("train.ds_offload") else ""
args["deepspeed"] = os.path.join(DEFAULT_CACHE_DIR, f"ds_z{ds_stage}_{ds_offload}config.json")
return args
def _parse_eval_args(self, data: dict["Component", Any]) -> dict[str, Any]:
r"""Build and validate the evaluation arguments."""
get = lambda elem_id: data[self.manager.get_elem_by_id(elem_id)]
model_name, finetuning_type = get("top.model_name"), get("top.finetuning_type")
user_config = load_config()
args = dict(
stage="sft",
model_name_or_path=get("top.model_path"),
cache_dir=user_config.get("cache_dir", None),
preprocessing_num_workers=16,
finetuning_type=finetuning_type,
quantization_method=get("top.quantization_method"),
template=get("top.template"),
rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") != "none" else None,
flash_attn="fa2" if get("top.booster") == "flashattn2" else "auto",
use_unsloth=(get("top.booster") == "unsloth"),
dataset_dir=get("eval.dataset_dir"),
eval_dataset=",".join(get("eval.dataset")),
cutoff_len=get("eval.cutoff_len"),
max_samples=int(get("eval.max_samples")),
per_device_eval_batch_size=get("eval.batch_size"),
predict_with_generate=True,
report_to="none",
max_new_tokens=get("eval.max_new_tokens"),
top_p=get("eval.top_p"),
temperature=get("eval.temperature"),
output_dir=get_save_dir(model_name, finetuning_type, get("eval.output_dir")),
trust_remote_code=True,
ddp_timeout=180000000,
)
if get("eval.predict"):
args["do_predict"] = True
else:
args["do_eval"] = True
# checkpoints
if get("top.checkpoint_path"):
if finetuning_type in PEFT_METHODS: # list
args["adapter_name_or_path"] = ",".join(
[get_save_dir(model_name, finetuning_type, adapter) for adapter in get("top.checkpoint_path")]
)
else: # str
args["model_name_or_path"] = get_save_dir(model_name, finetuning_type, get("top.checkpoint_path"))
# quantization
if get("top.quantization_bit") != "none":
args["quantization_bit"] = int(get("top.quantization_bit"))
args["quantization_method"] = get("top.quantization_method")
args["double_quantization"] = not is_torch_npu_available()
return args
def _preview(self, data: dict["Component", Any], do_train: bool) -> Generator[dict["Component", str], None, None]:
r"""Preview the training commands."""
output_box = self.manager.get_elem_by_id("{}.output_box".format("train" if do_train else "eval"))
error = self._initialize(data, do_train, from_preview=True)
if error:
gr.Warning(error)
yield {output_box: error}
else:
args = self._parse_train_args(data) if do_train else self._parse_eval_args(data)
yield {output_box: gen_cmd(args)}
def _launch(self, data: dict["Component", Any], do_train: bool) -> Generator[dict["Component", Any], None, None]:
r"""Start the training process."""
output_box = self.manager.get_elem_by_id("{}.output_box".format("train" if do_train else "eval"))
error = self._initialize(data, do_train, from_preview=False)
if error:
gr.Warning(error)
yield {output_box: error}
else:
self.do_train, self.running_data = do_train, data
args = self._parse_train_args(data) if do_train else self._parse_eval_args(data)
os.makedirs(args["output_dir"], exist_ok=True)
save_args(os.path.join(args["output_dir"], LLAMABOARD_CONFIG), self._build_config_dict(data))
env = deepcopy(os.environ)
env["LLAMABOARD_ENABLED"] = "1"
env["LLAMABOARD_WORKDIR"] = args["output_dir"]
if args.get("deepspeed", None) is not None:
env["FORCE_TORCHRUN"] = "1"
# NOTE: DO NOT USE shell=True to avoid security risk
self.trainer = Popen(["llamafactory-cli", "train", save_cmd(args)], env=env, stderr=PIPE, text=True)
yield from self.monitor()
def _build_config_dict(self, data: dict["Component", Any]) -> dict[str, Any]:
r"""Build a dictionary containing the current training configuration."""
config_dict = {}
skip_ids = ["top.lang", "top.model_path", "train.output_dir", "train.config_path"]
for elem, value in data.items():
elem_id = self.manager.get_id_by_elem(elem)
if elem_id not in skip_ids:
config_dict[elem_id] = value
return config_dict
def preview_train(self, data):
yield from self._preview(data, do_train=True)
def preview_eval(self, data):
yield from self._preview(data, do_train=False)
def run_train(self, data):
yield from self._launch(data, do_train=True)
def run_eval(self, data):
yield from self._launch(data, do_train=False)
def monitor(self):
r"""Monitorgit the training progress and logs."""
self.aborted = False
self.running = True
get = lambda elem_id: self.running_data[self.manager.get_elem_by_id(elem_id)]
lang, model_name, finetuning_type = get("top.lang"), get("top.model_name"), get("top.finetuning_type")
output_dir = get("{}.output_dir".format("train" if self.do_train else "eval"))
output_path = get_save_dir(model_name, finetuning_type, output_dir)
output_box = self.manager.get_elem_by_id("{}.output_box".format("train" if self.do_train else "eval"))
progress_bar = self.manager.get_elem_by_id("{}.progress_bar".format("train" if self.do_train else "eval"))
loss_viewer = self.manager.get_elem_by_id("train.loss_viewer") if self.do_train else None
swanlab_link = self.manager.get_elem_by_id("train.swanlab_link") if self.do_train else None
running_log = ""
return_code = -1
while return_code == -1:
if self.aborted:
yield {
output_box: ALERTS["info_aborting"][lang],
progress_bar: gr.Slider(visible=False),
}
else:
running_log, running_progress, running_info = get_trainer_info(lang, output_path, self.do_train)
return_dict = {
output_box: running_log,
progress_bar: running_progress,
}
if "loss_viewer" in running_info:
return_dict[loss_viewer] = running_info["loss_viewer"]
if "swanlab_link" in running_info:
return_dict[swanlab_link] = running_info["swanlab_link"]
yield return_dict
try:
stderr = self.trainer.communicate(timeout=2)[1]
return_code = self.trainer.returncode
except TimeoutExpired:
continue
if return_code == 0 or self.aborted:
finish_info = ALERTS["info_finished"][lang]
if self.do_train:
finish_log = ALERTS["info_finished"][lang] + "\n\n" + running_log
else:
finish_log = load_eval_results(os.path.join(output_path, "all_results.json")) + "\n\n" + running_log
else:
print(stderr)
finish_info = ALERTS["err_failed"][lang]
finish_log = ALERTS["err_failed"][lang] + f" Exit code: {return_code}\n\n```\n{stderr}\n```\n"
self._finalize(lang, finish_info)
return_dict = {output_box: finish_log, progress_bar: gr.Slider(visible=False)}
yield return_dict
def save_args(self, data):
r"""Save the training configuration to config path."""
output_box = self.manager.get_elem_by_id("train.output_box")
error = self._initialize(data, do_train=True, from_preview=True)
if error:
gr.Warning(error)
return {output_box: error}
lang = data[self.manager.get_elem_by_id("top.lang")]
config_path = data[self.manager.get_elem_by_id("train.config_path")]
os.makedirs(DEFAULT_CONFIG_DIR, exist_ok=True)
save_path = os.path.join(DEFAULT_CONFIG_DIR, config_path)
save_args(save_path, self._build_config_dict(data))
return {output_box: ALERTS["info_config_saved"][lang] + save_path}
def load_args(self, lang: str, config_path: str):
r"""Load the training configuration from config path."""
output_box = self.manager.get_elem_by_id("train.output_box")
config_dict = load_args(os.path.join(DEFAULT_CONFIG_DIR, config_path))
if config_dict is None:
gr.Warning(ALERTS["err_config_not_found"][lang])
return {output_box: ALERTS["err_config_not_found"][lang]}
output_dict: dict[Component, Any] = {output_box: ALERTS["info_config_loaded"][lang]}
for elem_id, value in config_dict.items():
output_dict[self.manager.get_elem_by_id(elem_id)] = value
return output_dict
def check_output_dir(self, lang: str, model_name: str, finetuning_type: str, output_dir: str):
r"""Restore the training status if output_dir exists."""
output_box = self.manager.get_elem_by_id("train.output_box")
output_dict: dict[Component, Any] = {output_box: LOCALES["output_box"][lang]["value"]}
if model_name and output_dir and os.path.isdir(get_save_dir(model_name, finetuning_type, output_dir)):
gr.Warning(ALERTS["warn_output_dir_exists"][lang])
output_dict[output_box] = ALERTS["warn_output_dir_exists"][lang]
output_dir = get_save_dir(model_name, finetuning_type, output_dir)
config_dict = load_args(os.path.join(output_dir, LLAMABOARD_CONFIG)) # load llamaboard config
for elem_id, value in config_dict.items():
output_dict[self.manager.get_elem_by_id(elem_id)] = value
return output_dict
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/common.py | src/llamafactory/webui/common.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
import signal
from collections import defaultdict
from datetime import datetime
from typing import Any
from psutil import Process
from yaml import safe_dump, safe_load
from ..extras import logging
from ..extras.constants import (
DATA_CONFIG,
DEFAULT_TEMPLATE,
MULTIMODAL_SUPPORTED_MODELS,
SUPPORTED_MODELS,
TRAINING_ARGS,
DownloadSource,
)
from ..extras.misc import use_modelscope, use_openmind
logger = logging.get_logger(__name__)
DEFAULT_CACHE_DIR = "llamaboard_cache"
DEFAULT_CONFIG_DIR = "llamaboard_config"
DEFAULT_DATA_DIR = "data"
DEFAULT_SAVE_DIR = "saves"
USER_CONFIG = "user_config.yaml"
def abort_process(pid: int) -> None:
r"""Abort the processes recursively in a bottom-up way."""
try:
children = Process(pid).children()
if children:
for child in children:
abort_process(child.pid)
os.kill(pid, signal.SIGABRT)
except Exception:
pass
def get_save_dir(*paths: str) -> os.PathLike:
r"""Get the path to saved model checkpoints."""
if os.path.sep in paths[-1]:
logger.warning_rank0("Found complex path, some features may be not available.")
return paths[-1]
paths = (path.replace(" ", "").strip() for path in paths)
return os.path.join(DEFAULT_SAVE_DIR, *paths)
def _get_config_path() -> os.PathLike:
r"""Get the path to user config."""
return os.path.join(DEFAULT_CACHE_DIR, USER_CONFIG)
def load_config() -> dict[str, str | dict[str, Any]]:
r"""Load user config if exists."""
try:
with open(_get_config_path(), encoding="utf-8") as f:
return safe_load(f)
except Exception:
return {"lang": None, "hub_name": None, "last_model": None, "path_dict": {}, "cache_dir": None}
def save_config(
lang: str, hub_name: str | None = None, model_name: str | None = None, model_path: str | None = None
) -> None:
r"""Save user config."""
os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True)
user_config = load_config()
user_config["lang"] = lang or user_config["lang"]
if hub_name:
user_config["hub_name"] = hub_name
if model_name:
user_config["last_model"] = model_name
if model_name and model_path:
user_config["path_dict"][model_name] = model_path
with open(_get_config_path(), "w", encoding="utf-8") as f:
safe_dump(user_config, f)
def get_model_path(model_name: str) -> str:
r"""Get the model path according to the model name."""
user_config = load_config()
path_dict: dict[DownloadSource, str] = SUPPORTED_MODELS.get(model_name, defaultdict(str))
model_path = user_config["path_dict"].get(model_name, "") or path_dict.get(DownloadSource.DEFAULT, "")
if (
use_modelscope()
and path_dict.get(DownloadSource.MODELSCOPE)
and model_path == path_dict.get(DownloadSource.DEFAULT)
): # replace hf path with ms path
model_path = path_dict.get(DownloadSource.MODELSCOPE)
if (
use_openmind()
and path_dict.get(DownloadSource.OPENMIND)
and model_path == path_dict.get(DownloadSource.DEFAULT)
): # replace hf path with om path
model_path = path_dict.get(DownloadSource.OPENMIND)
return model_path
def get_template(model_name: str) -> str:
r"""Get the template name if the model is a chat/distill/instruct model."""
return DEFAULT_TEMPLATE.get(model_name, "default")
def get_time() -> str:
r"""Get current date and time."""
return datetime.now().strftime(r"%Y-%m-%d-%H-%M-%S")
def is_multimodal(model_name: str) -> bool:
r"""Judge if the model is a vision language model."""
return model_name in MULTIMODAL_SUPPORTED_MODELS
def load_dataset_info(dataset_dir: str) -> dict[str, dict[str, Any]]:
r"""Load dataset_info.json."""
if dataset_dir == "ONLINE" or dataset_dir.startswith("REMOTE:"):
logger.info_rank0(f"dataset_dir is {dataset_dir}, using online dataset.")
return {}
try:
with open(os.path.join(dataset_dir, DATA_CONFIG), encoding="utf-8") as f:
return json.load(f)
except Exception as err:
logger.warning_rank0(f"Cannot open {os.path.join(dataset_dir, DATA_CONFIG)} due to {str(err)}.")
return {}
def load_args(config_path: str) -> dict[str, Any] | None:
r"""Load the training configuration from config path."""
try:
with open(config_path, encoding="utf-8") as f:
return safe_load(f)
except Exception:
return None
def save_args(config_path: str, config_dict: dict[str, Any]) -> None:
r"""Save the training configuration to config path."""
with open(config_path, "w", encoding="utf-8") as f:
safe_dump(config_dict, f)
def _clean_cmd(args: dict[str, Any]) -> dict[str, Any]:
r"""Remove args with NoneType or False or empty string value."""
no_skip_keys = [
"packing",
"enable_thinking",
"use_reentrant_gc",
"double_quantization",
"freeze_vision_tower",
"freeze_multi_modal_projector",
]
return {k: v for k, v in args.items() if (k in no_skip_keys) or (v is not None and v is not False and v != "")}
def gen_cmd(args: dict[str, Any]) -> str:
r"""Generate CLI commands for previewing."""
cmd_lines = ["llamafactory-cli train "]
for k, v in _clean_cmd(args).items():
if isinstance(v, dict):
cmd_lines.append(f" --{k} {json.dumps(v, ensure_ascii=False)} ")
elif isinstance(v, list):
cmd_lines.append(f" --{k} {' '.join(map(str, v))} ")
else:
cmd_lines.append(f" --{k} {str(v)} ")
if os.name == "nt":
cmd_text = "`\n".join(cmd_lines)
else:
cmd_text = "\\\n".join(cmd_lines)
cmd_text = f"```bash\n{cmd_text}\n```"
return cmd_text
def save_cmd(args: dict[str, Any]) -> str:
r"""Save CLI commands to launch training."""
output_dir = args["output_dir"]
os.makedirs(output_dir, exist_ok=True)
with open(os.path.join(output_dir, TRAINING_ARGS), "w", encoding="utf-8") as f:
safe_dump(_clean_cmd(args), f)
return os.path.join(output_dir, TRAINING_ARGS)
def load_eval_results(path: os.PathLike) -> str:
r"""Get scores after evaluation."""
with open(path, encoding="utf-8") as f:
result = json.dumps(json.load(f), indent=4)
return f"```json\n{result}\n```\n"
def calculate_pixels(pixels: str) -> int:
r"""Calculate the number of pixels from the expression."""
if "*" in pixels:
return int(pixels.split("*")[0]) * int(pixels.split("*")[1])
else:
return int(pixels)
def create_ds_config() -> None:
r"""Create deepspeed config in the current directory."""
os.makedirs(DEFAULT_CACHE_DIR, exist_ok=True)
ds_config = {
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"zero_allow_untested_optimizer": True,
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1,
},
"bf16": {"enabled": "auto"},
}
offload_config = {
"device": "cpu",
"pin_memory": True,
}
ds_config["zero_optimization"] = {
"stage": 2,
"allgather_partitions": True,
"allgather_bucket_size": 5e8,
"overlap_comm": False,
"reduce_scatter": True,
"reduce_bucket_size": 5e8,
"contiguous_gradients": True,
"round_robin_gradients": True,
}
with open(os.path.join(DEFAULT_CACHE_DIR, "ds_z2_config.json"), "w", encoding="utf-8") as f:
json.dump(ds_config, f, indent=2)
ds_config["zero_optimization"]["offload_optimizer"] = offload_config
with open(os.path.join(DEFAULT_CACHE_DIR, "ds_z2_offload_config.json"), "w", encoding="utf-8") as f:
json.dump(ds_config, f, indent=2)
ds_config["zero_optimization"] = {
"stage": 3,
"overlap_comm": False,
"contiguous_gradients": True,
"sub_group_size": 1e9,
"reduce_bucket_size": "auto",
"stage3_prefetch_bucket_size": "auto",
"stage3_param_persistence_threshold": "auto",
"stage3_max_live_parameters": 1e9,
"stage3_max_reuse_distance": 1e9,
"stage3_gather_16bit_weights_on_model_save": True,
}
with open(os.path.join(DEFAULT_CACHE_DIR, "ds_z3_config.json"), "w", encoding="utf-8") as f:
json.dump(ds_config, f, indent=2)
ds_config["zero_optimization"]["offload_optimizer"] = offload_config
ds_config["zero_optimization"]["offload_param"] = offload_config
with open(os.path.join(DEFAULT_CACHE_DIR, "ds_z3_offload_config.json"), "w", encoding="utf-8") as f:
json.dump(ds_config, f, indent=2)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/locales.py | src/llamafactory/webui/locales.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
LOCALES = {
"title": {
"en": {
"value": "<h1><center>🦙🏭LLaMA Factory: Unified Efficient Fine-Tuning of 100+ LLMs</center></h1>",
},
"ru": {
"value": "<h1><center>🦙🏭LLaMA Factory: Унифицированная эффективная тонкая настройка 100+ LLMs</center></h1>",
},
"zh": {
"value": "<h1><center>🦙🏭LLaMA Factory: 一站式大模型高效微调平台</center></h1>",
},
"ko": {
"value": "<h1><center>🦙🏭LLaMA Factory: 100+ LLMs를 위한 통합 효율적인 튜닝</center></h1>",
},
"ja": {
"value": "<h1><center>🦙🏭LLaMA Factory: 100+ LLMs の統合効率的なチューニング</center></h1>",
},
},
"subtitle": {
"en": {
"value": (
"<h3><center>Visit <a href='https://github.com/hiyouga/LLaMA-Factory' target='_blank'>"
"GitHub Page</a> <a href='https://llamafactory.readthedocs.io/en/latest/' target='_blank'>"
"Documentation</a> <a href='https://blog.llamafactory.net/en/' target='_blank'>"
"Blog</a></center></h3>"
),
},
"ru": {
"value": (
"<h3><center>Посетить <a href='https://github.com/hiyouga/LLaMA-Factory' target='_blank'>"
"страницу GitHub</a> <a href='https://llamafactory.readthedocs.io/en/latest/' target='_blank'>"
"Документацию</a> <a href='https://blog.llamafactory.net/en/' target='_blank'>"
"Блог</a></center></h3>"
),
},
"zh": {
"value": (
"<h3><center>访问 <a href='https://github.com/hiyouga/LLaMA-Factory' target='_blank'>"
"GitHub 主页</a> <a href='https://llamafactory.readthedocs.io/zh-cn/latest/' target='_blank'>"
"官方文档</a> <a href='https://blog.llamafactory.net/' target='_blank'>"
"博客</a></center></h3>"
),
},
"ko": {
"value": (
"<h3><center><a href='https://github.com/hiyouga/LLaMA-Factory' target='_blank'>"
"GitHub 페이지</a> <a href='https://llamafactory.readthedocs.io/en/latest/' target='_blank'>"
"공식 문서</a> <a href='https://blog.llamafactory.net/en/' target='_blank'>"
"블로그</a>를 방문하세요.</center></h3>"
),
},
"ja": {
"value": (
"<h3><center><a href='https://github.com/hiyouga/LLaMA-Factory' target='_blank'>"
"GitHub ページ</a> <a href='https://llamafactory.readthedocs.io/en/latest/' target='_blank'>"
"ドキュメント</a> <a href='https://blog.llamafactory.net/en/' target='_blank'>"
"ブログ</a>にアクセスする</center></h3>"
),
},
},
"lang": {
"en": {
"label": "Language",
},
"ru": {
"label": "Язык",
},
"zh": {
"label": "语言",
},
"ko": {
"label": "언어",
},
"ja": {
"label": "言語",
},
},
"model_name": {
"en": {
"label": "Model name",
"info": "Input the initial name to search for the model.",
},
"ru": {
"label": "Название модели",
"info": "Введите начальное имя для поиска модели.",
},
"zh": {
"label": "模型名称",
"info": "输入首单词以检索模型。",
},
"ko": {
"label": "모델 이름",
"info": "모델을 검색할 초기 이름을 입력하세요.",
},
"ja": {
"label": "モデル名",
"info": "モデルを検索するための初期名を入力してください。",
},
},
"model_path": {
"en": {
"label": "Model path",
"info": "Path to pretrained model or model identifier from Hugging Face.",
},
"ru": {
"label": "Путь к модели",
"info": "Путь к предварительно обученной модели или идентификатор модели от Hugging Face.",
},
"zh": {
"label": "模型路径",
"info": "本地模型的文件路径或 Hugging Face 的模型标识符。",
},
"ko": {
"label": "모델 경로",
"info": "사전 훈련된 모델의 경로 또는 Hugging Face의 모델 식별자.",
},
"ja": {
"label": "モデルパス",
"info": "事前学習済みモデルへのパス、または Hugging Face のモデル識別子。",
},
},
"hub_name": {
"en": {
"label": "Hub name",
"info": "Choose the model download source.",
},
"ru": {
"label": "Имя хаба",
"info": "Выберите источник загрузки модели.",
},
"zh": {
"label": "模型下载源",
"info": "选择模型下载源。（网络受限环境推荐使用 ModelScope）",
},
"ko": {
"label": "모델 다운로드 소스",
"info": "모델 다운로드 소스를 선택하세요.",
},
"ja": {
"label": "モデルダウンロードソース",
"info": "モデルをダウンロードするためのソースを選択してください。",
},
},
"finetuning_type": {
"en": {
"label": "Finetuning method",
},
"ru": {
"label": "Метод дообучения",
},
"zh": {
"label": "微调方法",
},
"ko": {
"label": "파인튜닝 방법",
},
"ja": {
"label": "ファインチューニング方法",
},
},
"checkpoint_path": {
"en": {
"label": "Checkpoint path",
},
"ru": {
"label": "Путь контрольной точки",
},
"zh": {
"label": "检查点路径",
},
"ko": {
"label": "체크포인트 경로",
},
"ja": {
"label": "チェックポイントパス",
},
},
"quantization_bit": {
"en": {
"label": "Quantization bit",
"info": "Enable quantization (QLoRA).",
},
"ru": {
"label": "Уровень квантования",
"info": "Включить квантование (QLoRA).",
},
"zh": {
"label": "量化等级",
"info": "启用量化（QLoRA）。",
},
"ko": {
"label": "양자화 비트",
"info": "양자화 활성화 (QLoRA).",
},
"ja": {
"label": "量子化ビット",
"info": "量子化を有効にする (QLoRA)。",
},
},
"quantization_method": {
"en": {
"label": "Quantization method",
"info": "Quantization algorithm to use.",
},
"ru": {
"label": "Метод квантования",
"info": "Алгоритм квантования, который следует использовать.",
},
"zh": {
"label": "量化方法",
"info": "使用的量化算法。",
},
"ko": {
"label": "양자화 방법",
"info": "사용할 양자화 알고리즘.",
},
"ja": {
"label": "量子化方法",
"info": "使用する量子化アルゴリズム。",
},
},
"template": {
"en": {
"label": "Chat template",
"info": "The chat template used in constructing prompts.",
},
"ru": {
"label": "Шаблон чата",
"info": "Шаблон чата используемый для составления подсказок.",
},
"zh": {
"label": "对话模板",
"info": "构建提示词时使用的模板。",
},
"ko": {
"label": "채팅 템플릿",
"info": "프롬프트 작성에 사용되는 채팅 템플릿.",
},
"ja": {
"label": "チャットテンプレート",
"info": "プロンプトの構築に使用されるチャットテンプレート。",
},
},
"rope_scaling": {
"en": {
"label": "RoPE scaling",
"info": "RoPE scaling method to use.",
},
"ru": {
"label": "Масштабирование RoPE",
"info": "Метод масштабирования RoPE для использования.",
},
"zh": {"label": "RoPE 插值方法", "info": "RoPE 插值时使用的方法。"},
"ko": {
"label": "RoPE 스케일링",
"info": "사용할 RoPE 스케일링 방법.",
},
"ja": {
"label": "RoPE スケーリング",
"info": "使用する RoPE スケーリング方法。",
},
},
"booster": {
"en": {
"label": "Booster",
"info": "Approach used to boost training speed.",
},
"ru": {
"label": "Ускоритель",
"info": "Подход, используемый для ускорения обучения.",
},
"zh": {"label": "加速方式", "info": "使用的加速方法。"},
"ko": {
"label": "부스터",
"info": "훈련 속도를 향상시키기 위해 사용된 접근 방식.",
},
"ja": {
"label": "ブースター",
"info": "トレーニング速度を向上させるためのアプローチ。",
},
},
"training_stage": {
"en": {
"label": "Stage",
"info": "The stage to perform in training.",
},
"ru": {
"label": "Этап",
"info": "Этап выполнения обучения.",
},
"zh": {
"label": "训练阶段",
"info": "目前采用的训练方式。",
},
"ko": {
"label": "학습 단계",
"info": "수행할 학습 방법.",
},
"ja": {
"label": "ステージ",
"info": "トレーニングで実行するステージ。",
},
},
"dataset_dir": {
"en": {
"label": "Data dir",
"info": "Path to the data directory.",
},
"ru": {
"label": "Директория данных",
"info": "Путь к директории данных.",
},
"zh": {
"label": "数据路径",
"info": "数据文件夹的路径。",
},
"ko": {
"label": "데이터 디렉토리",
"info": "데이터 디렉토리의 경로.",
},
"ja": {
"label": "データディレクトリ",
"info": "データディレクトリへのパス。",
},
},
"dataset": {
"en": {
"label": "Dataset",
},
"ru": {
"label": "Набор данных",
},
"zh": {
"label": "数据集",
},
"ko": {
"label": "데이터셋",
},
"ja": {
"label": "データセット",
},
},
"data_preview_btn": {
"en": {
"value": "Preview dataset",
},
"ru": {
"value": "Просмотреть набор данных",
},
"zh": {
"value": "预览数据集",
},
"ko": {
"value": "데이터셋 미리보기",
},
"ja": {
"value": "データセットをプレビュー",
},
},
"preview_count": {
"en": {
"label": "Count",
},
"ru": {
"label": "Количество",
},
"zh": {
"label": "数量",
},
"ko": {
"label": "개수",
},
"ja": {
"label": "カウント",
},
},
"page_index": {
"en": {
"label": "Page",
},
"ru": {
"label": "Страница",
},
"zh": {
"label": "页数",
},
"ko": {
"label": "페이지",
},
"ja": {
"label": "ページ",
},
},
"prev_btn": {
"en": {
"value": "Prev",
},
"ru": {
"value": "Предыдущая",
},
"zh": {
"value": "上一页",
},
"ko": {
"value": "이전",
},
"ja": {
"value": "前へ",
},
},
"next_btn": {
"en": {
"value": "Next",
},
"ru": {
"value": "Следующая",
},
"zh": {
"value": "下一页",
},
"ko": {
"value": "다음",
},
"ja": {
"value": "次へ",
},
},
"close_btn": {
"en": {
"value": "Close",
},
"ru": {
"value": "Закрыть",
},
"zh": {
"value": "关闭",
},
"ko": {
"value": "닫기",
},
"ja": {
"value": "閉じる",
},
},
"preview_samples": {
"en": {
"label": "Samples",
},
"ru": {
"label": "Примеры",
},
"zh": {
"label": "样例",
},
"ko": {
"label": "샘플",
},
"ja": {
"label": "サンプル",
},
},
"learning_rate": {
"en": {
"label": "Learning rate",
"info": "Initial learning rate for AdamW.",
},
"ru": {
"label": "Скорость обучения",
"info": "Начальная скорость обучения для AdamW.",
},
"zh": {
"label": "学习率",
"info": "AdamW 优化器的初始学习率。",
},
"ko": {
"label": "학습률",
"info": "AdamW의 초기 학습률.",
},
"ja": {
"label": "学習率",
"info": "AdamW の初期学習率。",
},
},
"num_train_epochs": {
"en": {
"label": "Epochs",
"info": "Total number of training epochs to perform.",
},
"ru": {
"label": "Эпохи",
"info": "Общее количество эпох обучения.",
},
"zh": {
"label": "训练轮数",
"info": "需要执行的训练总轮数。",
},
"ko": {
"label": "에포크",
"info": "수행할 총 학습 에포크 수.",
},
"ja": {
"label": "エポック数",
"info": "実行するトレーニングの総エポック数。",
},
},
"max_grad_norm": {
"en": {
"label": "Maximum gradient norm",
"info": "Norm for gradient clipping.",
},
"ru": {
"label": "Максимальная норма градиента",
"info": "Норма для обрезки градиента.",
},
"zh": {
"label": "最大梯度范数",
"info": "用于梯度裁剪的范数。",
},
"ko": {
"label": "최대 그레디언트 노름(norm)",
"info": "그레디언트 클리핑을 위한 노름(norm).",
},
"ja": {
"label": "最大勾配ノルム",
"info": "勾配クリッピングのためのノルム。",
},
},
"max_samples": {
"en": {
"label": "Max samples",
"info": "Maximum samples per dataset.",
},
"ru": {
"label": "Максимальное количество образцов",
"info": "Максимальное количество образцов на набор данных.",
},
"zh": {
"label": "最大样本数",
"info": "每个数据集的最大样本数。",
},
"ko": {
"label": "최대 샘플 수",
"info": "데이터셋 당 최대 샘플 수.",
},
"ja": {
"label": "最大サンプル数",
"info": "データセットごとの最大サンプル数。",
},
},
"compute_type": {
"en": {
"label": "Compute type",
"info": "Whether to use mixed precision training.",
},
"ru": {
"label": "Тип вычислений",
"info": "Использовать ли обучение смешанной точности.",
},
"zh": {
"label": "计算类型",
"info": "是否使用混合精度训练。",
},
"ko": {
"label": "연산 유형",
"info": "혼합 정밀도 훈련을 사용할지 여부.",
},
"ja": {
"label": "計算タイプ",
"info": "混合精度トレーニングを使用するかどうか。",
},
},
"cutoff_len": {
"en": {
"label": "Cutoff length",
"info": "Max tokens in input sequence.",
},
"ru": {
"label": "Длина обрезки",
"info": "Максимальное количество токенов во входной последовательности.",
},
"zh": {
"label": "截断长度",
"info": "输入序列分词后的最大长度。",
},
"ko": {
"label": "컷오프 길이",
"info": "입력 시퀀스의 최대 토큰 수.",
},
"ja": {
"label": "カットオフ長",
"info": "入力シーケンスの最大トークン数。",
},
},
"batch_size": {
"en": {
"label": "Batch size",
"info": "Number of samples processed on each GPU.",
},
"ru": {
"label": "Размер пакета",
"info": "Количество образцов для обработки на каждом GPU.",
},
"zh": {
"label": "批处理大小",
"info": "每个 GPU 处理的样本数量。",
},
"ko": {
"label": "배치 크기",
"info": "각 GPU에서 처리되는 샘플 수.",
},
"ja": {
"label": "バッチサイズ",
"info": "各 GPU で処理されるサンプル数。",
},
},
"gradient_accumulation_steps": {
"en": {
"label": "Gradient accumulation",
"info": "Number of steps for gradient accumulation.",
},
"ru": {
"label": "Накопление градиента",
"info": "Количество шагов накопления градиента.",
},
"zh": {
"label": "梯度累积",
"info": "梯度累积的步数。",
},
"ko": {
"label": "그레디언트 누적",
"info": "그레디언트 누적 단계 수.",
},
"ja": {
"label": "勾配累積",
"info": "勾配累積のステップ数。",
},
},
"val_size": {
"en": {
"label": "Val size",
"info": "Percentage of validation set from the entire dataset.",
},
"ru": {
"label": "Размер валидации",
"info": "Пропорция данных в наборе для разработки.",
},
"zh": {
"label": "验证集比例",
"info": "验证集占全部样本的百分比。",
},
"ko": {
"label": "검증 데이터셋 크기",
"info": "개발 데이터셋에서 검증 데이터의 비율.",
},
"ja": {
"label": "検証セットサイズ",
"info": "データセット全体に対する検証セットの割合。",
},
},
"lr_scheduler_type": {
"en": {
"label": "LR scheduler",
"info": "Name of the learning rate scheduler.",
},
"ru": {
"label": "Планировщик скорости обучения",
"info": "Название планировщика скорости обучения.",
},
"zh": {
"label": "学习率调节器",
"info": "学习率调度器的名称。",
},
"ko": {
"label": "LR 스케줄러",
"info": "학습률 스케줄러의 이름.",
},
"ja": {
"label": "学習率スケジューラ",
"info": "学習率スケジューラの名前。",
},
},
"extra_tab": {
"en": {
"label": "Extra configurations",
},
"ru": {
"label": "Дополнительные конфигурации",
},
"zh": {
"label": "其它参数设置",
},
"ko": {
"label": "추가 구성(configuration)",
},
"ja": {
"label": "追加設定",
},
},
"logging_steps": {
"en": {
"label": "Logging steps",
"info": "Number of steps between two logs.",
},
"ru": {
"label": "Шаги логирования",
"info": "Количество шагов между двумя записями в журнале.",
},
"zh": {
"label": "日志间隔",
"info": "每两次日志输出间的更新步数。",
},
"ko": {
"label": "로깅 스텝",
"info": "이전 로깅과 다음 로깅 간 스텝 수.",
},
"ja": {
"label": "ロギングステップ",
"info": "2 つのログ間のステップ数。",
},
},
"save_steps": {
"en": {
"label": "Save steps",
"info": "Number of steps between two checkpoints.",
},
"ru": {
"label": "Шаги сохранения",
"info": "Количество шагов между двумя контрольными точками.",
},
"zh": {
"label": "保存间隔",
"info": "每两次断点保存间的更新步数。",
},
"ko": {
"label": "저장 스텝",
"info": "이전 체크포인트와 다음 체크포인트 사이의 스텝 수.",
},
"ja": {
"label": "保存ステップ",
"info": "2 つのチェックポイント間のステップ数。",
},
},
"warmup_steps": {
"en": {
"label": "Warmup steps",
"info": "Number of steps used for warmup.",
},
"ru": {
"label": "Шаги прогрева",
"info": "Количество шагов, используемых для прогрева.",
},
"zh": {
"label": "预热步数",
"info": "学习率预热采用的步数。",
},
"ko": {
"label": "Warmup 스텝",
"info": "Warmup에 사용되는 스텝 수.",
},
"ja": {
"label": "ウォームアップステップ",
"info": "ウォームアップに使用されるステップ数。",
},
},
"neftune_alpha": {
"en": {
"label": "NEFTune alpha",
"info": "Magnitude of noise adding to embedding vectors.",
},
"ru": {
"label": "NEFTune alpha",
"info": "Величина шума, добавляемого к векторам вложений.",
},
"zh": {
"label": "NEFTune 噪声参数",
"info": "嵌入向量所添加的噪声大小。",
},
"ko": {
"label": "NEFTune 알파",
"info": "임베딩 벡터에 추가되는 노이즈의 크기.",
},
"ja": {
"label": "NEFTune alpha",
"info": "埋め込みベクトルに追加されるノイズの大きさ。",
},
},
"extra_args": {
"en": {
"label": "Extra arguments",
"info": "Extra arguments passed to the trainer in JSON format.",
},
"ru": {
"label": "Дополнительные аргументы",
"info": "Дополнительные аргументы, которые передаются тренеру в формате JSON.",
},
"zh": {
"label": "额外参数",
"info": "以 JSON 格式传递给训练器的额外参数。",
},
"ko": {
"label": "추가 인수",
"info": "JSON 형식으로 트레이너에게 전달할 추가 인수입니다.",
},
"ja": {
"label": "追加引数",
"info": "JSON 形式でトレーナーに渡される追加引数。",
},
},
"packing": {
"en": {
"label": "Pack sequences",
"info": "Pack sequences into samples of fixed length.",
},
"ru": {
"label": "Упаковка последовательностей",
"info": "Упаковка последовательностей в образцы фиксированной длины.",
},
"zh": {
"label": "序列打包",
"info": "将序列打包为等长样本。",
},
"ko": {
"label": "시퀀스 패킹",
"info": "고정된 길이의 샘플로 시퀀스를 패킹합니다.",
},
"ja": {
"label": "シーケンスパッキング",
"info": "シーケンスを固定長のサンプルにパッキングします。",
},
},
"neat_packing": {
"en": {
"label": "Use neat packing",
"info": "Avoid cross-attention between packed sequences.",
},
"ru": {
"label": "Используйте аккуратную упаковку",
"info": "избегайте перекрестного внимания между упакованными последовательностями.",
},
"zh": {
"label": "使用无污染打包",
"info": "避免打包后的序列产生交叉注意力。",
},
"ko": {
"label": "니트 패킹 사용",
"info": "패킹된 시퀀스 간의 크로스 어텐션을 피합니다.",
},
"ja": {
"label": "無汚染パッキングを使用",
"info": "パッキング後のシーケンス間のクロスアテンションを避けます。",
},
},
"train_on_prompt": {
"en": {
"label": "Train on prompt",
"info": "Disable the label mask on the prompt (only for SFT).",
},
"ru": {
"label": "Тренировка на подсказке",
"info": "Отключить маску меток на подсказке (только для SFT).",
},
"zh": {
"label": "学习提示词",
"info": "不在提示词的部分添加掩码（仅适用于 SFT）。",
},
"ko": {
"label": "프롬프트도 학습",
"info": "프롬프트에서 라벨 마스킹을 비활성화합니다 (SFT에만 해당).",
},
"ja": {
"label": "プロンプトで学習",
"info": "プロンプト部分にマスクを追加しない（SFT のみ）。",
},
},
"mask_history": {
"en": {
"label": "Mask history",
"info": "Train on the last turn only (only for SFT).",
},
"ru": {
"label": "История масок",
"info": "Тренироваться только на последнем шаге (только для SFT).",
},
"zh": {
"label": "不学习历史对话",
"info": "仅学习最后一轮对话（仅适用于 SFT）。",
},
"ko": {
"label": "히스토리 마스킹",
"info": "대화 데이터의 마지막 턴만 학습합니다 (SFT에만 해당).",
},
"ja": {
"label": "履歴をマスク",
"info": "最後のターンのみを学習する（SFT のみ）。",
},
},
"resize_vocab": {
"en": {
"label": "Resize token embeddings",
"info": "Resize the tokenizer vocab and the embedding layers.",
},
"ru": {
"label": "Изменение размера токенных эмбеддингов",
"info": "Изменить размер словаря токенизатора и слоев эмбеддинга.",
},
"zh": {
"label": "更改词表大小",
"info": "更改分词器词表和嵌入层的大小。",
},
"ko": {
"label": "토큰 임베딩의 사이즈 조정",
"info": "토크나이저 어휘와 임베딩 레이어의 크기를 조정합니다.",
},
"ja": {
"label": "トークン埋め込みのサイズ変更",
"info": "トークナイザーの語彙と埋め込み層のサイズを変更します。",
},
},
"use_llama_pro": {
"en": {
"label": "Enable LLaMA Pro",
"info": "Make the parameters in the expanded blocks trainable.",
},
"ru": {
"label": "Включить LLaMA Pro",
"info": "Сделать параметры в расширенных блоках обучаемыми.",
},
"zh": {
"label": "使用 LLaMA Pro",
"info": "仅训练块扩展后的参数。",
},
"ko": {
"label": "LLaMA Pro 사용",
"info": "확장된 블록의 매개변수를 학습 가능하게 만듭니다.",
},
"ja": {
"label": "LLaMA Pro を有効化",
"info": "拡張ブロックのパラメータのみをトレーニングします。",
},
},
"enable_thinking": {
"en": {
"label": "Enable thinking",
"info": "Whether or not to enable thinking mode for reasoning models.",
},
"ru": {
"label": "Включить мысли",
"info": "Включить режим мысли для моделей решающего характера.",
},
"zh": {
"label": "启用思考模式",
"info": "是否启用推理模型的思考模式。",
},
"ko": {
"label": "생각 모드 활성화",
"info": "추론 모델의 생각 모드를 활성화할지 여부.",
},
"ja": {
"label": "思考モードを有効化",
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | true |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/control.py | src/llamafactory/webui/control.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from typing import Any
from transformers.trainer_utils import get_last_checkpoint
from ..extras.constants import (
CHECKPOINT_NAMES,
PEFT_METHODS,
RUNNING_LOG,
STAGES_USE_PAIR_DATA,
SWANLAB_CONFIG,
TRAINER_LOG,
TRAINING_STAGES,
)
from ..extras.packages import is_gradio_available, is_matplotlib_available
from ..extras.ploting import gen_loss_plot
from ..model import QuantizationMethod
from .common import DEFAULT_CONFIG_DIR, DEFAULT_DATA_DIR, get_model_path, get_save_dir, get_template, load_dataset_info
from .locales import ALERTS
if is_gradio_available():
import gradio as gr
def switch_hub(hub_name: str) -> None:
r"""Switch model hub.
Inputs: top.hub_name
"""
os.environ["USE_MODELSCOPE_HUB"] = "1" if hub_name == "modelscope" else "0"
os.environ["USE_OPENMIND_HUB"] = "1" if hub_name == "openmind" else "0"
def can_quantize(finetuning_type: str) -> "gr.Dropdown":
r"""Judge if the quantization is available in this finetuning type.
Inputs: top.finetuning_type
Outputs: top.quantization_bit
"""
if finetuning_type not in PEFT_METHODS:
return gr.Dropdown(value="none", interactive=False)
else:
return gr.Dropdown(interactive=True)
def can_quantize_to(quantization_method: str) -> "gr.Dropdown":
r"""Get the available quantization bits.
Inputs: top.quantization_method
Outputs: top.quantization_bit
"""
if quantization_method == QuantizationMethod.BNB:
available_bits = ["none", "8", "4"]
elif quantization_method == QuantizationMethod.HQQ:
available_bits = ["none", "8", "6", "5", "4", "3", "2", "1"]
elif quantization_method == QuantizationMethod.EETQ:
available_bits = ["none", "8"]
return gr.Dropdown(choices=available_bits)
def change_stage(training_stage: str = list(TRAINING_STAGES.keys())[0]) -> tuple[list[str], bool]:
r"""Modify states after changing the training stage.
Inputs: train.training_stage
Outputs: train.dataset, train.packing
"""
return [], TRAINING_STAGES[training_stage] == "pt"
def get_model_info(model_name: str) -> tuple[str, str]:
r"""Get the necessary information of this model.
Inputs: top.model_name
Outputs: top.model_path, top.template
"""
return get_model_path(model_name), get_template(model_name)
def check_template(lang: str, template: str) -> None:
r"""Check if an instruct model is used.
Please use queue=True to show the warning message.
Inputs: top.lang, top.template
"""
if template == "default":
gr.Warning(ALERTS["warn_no_instruct"][lang])
def get_trainer_info(lang: str, output_path: os.PathLike, do_train: bool) -> tuple[str, "gr.Slider", dict[str, Any]]:
r"""Get training infomation for monitor.
If do_train is True:
Inputs: top.lang, train.output_path
Outputs: train.output_box, train.progress_bar, train.loss_viewer, train.swanlab_link
If do_train is False:
Inputs: top.lang, eval.output_path
Outputs: eval.output_box, eval.progress_bar, None, None
"""
running_log = ""
running_progress = gr.Slider(visible=False)
running_info = {}
running_log_path = os.path.join(output_path, RUNNING_LOG)
if os.path.isfile(running_log_path):
with open(running_log_path, encoding="utf-8") as f:
running_log = "```\n" + f.read()[-20000:] + "\n```\n" # avoid lengthy log
trainer_log_path = os.path.join(output_path, TRAINER_LOG)
if os.path.isfile(trainer_log_path):
trainer_log: list[dict[str, Any]] = []
with open(trainer_log_path, encoding="utf-8") as f:
for line in f:
trainer_log.append(json.loads(line))
if len(trainer_log) != 0:
latest_log = trainer_log[-1]
percentage = latest_log["percentage"]
label = "Running {:d}/{:d}: {} < {}".format(
latest_log["current_steps"],
latest_log["total_steps"],
latest_log["elapsed_time"],
latest_log["remaining_time"],
)
running_progress = gr.Slider(label=label, value=percentage, visible=True)
if do_train and is_matplotlib_available():
running_info["loss_viewer"] = gr.Plot(gen_loss_plot(trainer_log))
swanlab_config_path = os.path.join(output_path, SWANLAB_CONFIG)
if os.path.isfile(swanlab_config_path):
with open(swanlab_config_path, encoding="utf-8") as f:
swanlab_public_config = json.load(f)
swanlab_link = swanlab_public_config["cloud"]["experiment_url"]
if swanlab_link is not None:
running_info["swanlab_link"] = gr.Markdown(
ALERTS["info_swanlab_link"][lang] + swanlab_link, visible=True
)
return running_log, running_progress, running_info
def list_checkpoints(model_name: str, finetuning_type: str) -> "gr.Dropdown":
r"""List all available checkpoints.
Inputs: top.model_name, top.finetuning_type
Outputs: top.checkpoint_path
"""
checkpoints = []
if model_name:
save_dir = get_save_dir(model_name, finetuning_type)
if save_dir and os.path.isdir(save_dir):
for checkpoint in os.listdir(save_dir):
if os.path.isdir(os.path.join(save_dir, checkpoint)) and any(
os.path.isfile(os.path.join(save_dir, checkpoint, name)) for name in CHECKPOINT_NAMES
):
checkpoints.append(checkpoint)
if finetuning_type in PEFT_METHODS:
return gr.Dropdown(value=[], choices=checkpoints, multiselect=True)
else:
return gr.Dropdown(value=None, choices=checkpoints, multiselect=False)
def list_config_paths(current_time: str) -> "gr.Dropdown":
r"""List all the saved configuration files.
Inputs: train.current_time
Outputs: train.config_path
"""
config_files = [f"{current_time}.yaml"]
if os.path.isdir(DEFAULT_CONFIG_DIR):
for file_name in os.listdir(DEFAULT_CONFIG_DIR):
if file_name.endswith(".yaml") and file_name not in config_files:
config_files.append(file_name)
return gr.Dropdown(choices=config_files)
def list_datasets(dataset_dir: str = None, training_stage: str = list(TRAINING_STAGES.keys())[0]) -> "gr.Dropdown":
r"""List all available datasets in the dataset dir for the training stage.
Inputs: *.dataset_dir, *.training_stage
Outputs: *.dataset
"""
dataset_info = load_dataset_info(dataset_dir if dataset_dir is not None else DEFAULT_DATA_DIR)
ranking = TRAINING_STAGES[training_stage] in STAGES_USE_PAIR_DATA
datasets = [k for k, v in dataset_info.items() if v.get("ranking", False) == ranking]
return gr.Dropdown(choices=datasets)
def list_output_dirs(model_name: str | None, finetuning_type: str, current_time: str) -> "gr.Dropdown":
r"""List all the directories that can resume from.
Inputs: top.model_name, top.finetuning_type, train.current_time
Outputs: train.output_dir
"""
output_dirs = [f"train_{current_time}"]
if model_name:
save_dir = get_save_dir(model_name, finetuning_type)
if save_dir and os.path.isdir(save_dir):
for folder in os.listdir(save_dir):
output_dir = os.path.join(save_dir, folder)
if os.path.isdir(output_dir) and get_last_checkpoint(output_dir) is not None:
output_dirs.append(folder)
return gr.Dropdown(choices=output_dirs)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/css.py | src/llamafactory/webui/css.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
CSS = r"""
.duplicate-button {
margin: auto !important;
color: white !important;
background: black !important;
border-radius: 100vh !important;
}
.thinking-summary {
padding: 8px !important;
}
.thinking-summary span {
border-radius: 4px !important;
padding: 4px !important;
cursor: pointer !important;
font-size: 14px !important;
background: rgb(245, 245, 245) !important;
}
.dark .thinking-summary span {
background: rgb(73, 73, 73) !important;
}
.thinking-container {
border-left: 2px solid #a6a6a6 !important;
padding-left: 10px !important;
margin: 4px 0 !important;
}
.thinking-container p {
color: #a6a6a6 !important;
}
.modal-box {
position: fixed !important;
top: 50%;
left: 50%;
transform: translate(-50%, -50%); /* center horizontally */
max-width: 1000px;
max-height: 750px;
overflow-y: auto;
background-color: var(--input-background-fill);
flex-wrap: nowrap !important;
border: 2px solid black !important;
z-index: 1000;
padding: 10px;
}
.dark .modal-box {
border: 2px solid white !important;
}
"""
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/interface.py | src/llamafactory/webui/interface.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import platform
from ..extras.misc import fix_proxy, is_env_enabled
from ..extras.packages import is_gradio_available
from .common import save_config
from .components import (
create_chat_box,
create_eval_tab,
create_export_tab,
create_footer,
create_infer_tab,
create_top,
create_train_tab,
)
from .css import CSS
from .engine import Engine
if is_gradio_available():
import gradio as gr
def create_ui(demo_mode: bool = False) -> "gr.Blocks":
engine = Engine(demo_mode=demo_mode, pure_chat=False)
hostname = os.getenv("HOSTNAME", os.getenv("COMPUTERNAME", platform.node())).split(".")[0]
with gr.Blocks(title=f"LLaMA Factory ({hostname})", css=CSS) as demo:
title = gr.HTML()
subtitle = gr.HTML()
if demo_mode:
gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")
engine.manager.add_elems("head", {"title": title, "subtitle": subtitle})
engine.manager.add_elems("top", create_top())
lang: gr.Dropdown = engine.manager.get_elem_by_id("top.lang")
with gr.Tab("Train"):
engine.manager.add_elems("train", create_train_tab(engine))
with gr.Tab("Evaluate & Predict"):
engine.manager.add_elems("eval", create_eval_tab(engine))
with gr.Tab("Chat"):
engine.manager.add_elems("infer", create_infer_tab(engine))
if not demo_mode:
with gr.Tab("Export"):
engine.manager.add_elems("export", create_export_tab(engine))
engine.manager.add_elems("footer", create_footer())
demo.load(engine.resume, outputs=engine.manager.get_elem_list(), concurrency_limit=None)
lang.change(engine.change_lang, [lang], engine.manager.get_elem_list(), queue=False)
lang.input(save_config, inputs=[lang], queue=False)
return demo
def create_web_demo() -> "gr.Blocks":
engine = Engine(pure_chat=True)
hostname = os.getenv("HOSTNAME", os.getenv("COMPUTERNAME", platform.node())).split(".")[0]
with gr.Blocks(title=f"LLaMA Factory Web Demo ({hostname})", css=CSS) as demo:
lang = gr.Dropdown(choices=["en", "ru", "zh", "ko", "ja"], scale=1)
engine.manager.add_elems("top", dict(lang=lang))
_, _, chat_elems = create_chat_box(engine, visible=True)
engine.manager.add_elems("infer", chat_elems)
demo.load(engine.resume, outputs=engine.manager.get_elem_list(), concurrency_limit=None)
lang.change(engine.change_lang, [lang], engine.manager.get_elem_list(), queue=False)
lang.input(save_config, inputs=[lang], queue=False)
return demo
def run_web_ui() -> None:
gradio_ipv6 = is_env_enabled("GRADIO_IPV6")
gradio_share = is_env_enabled("GRADIO_SHARE")
server_name = os.getenv("GRADIO_SERVER_NAME", "[::]" if gradio_ipv6 else "0.0.0.0")
print("Visit http://ip:port for Web UI, e.g., http://127.0.0.1:7860")
fix_proxy(ipv6_enabled=gradio_ipv6)
create_ui().queue().launch(share=gradio_share, server_name=server_name, inbrowser=True)
def run_web_demo() -> None:
gradio_ipv6 = is_env_enabled("GRADIO_IPV6")
gradio_share = is_env_enabled("GRADIO_SHARE")
server_name = os.getenv("GRADIO_SERVER_NAME", "[::]" if gradio_ipv6 else "0.0.0.0")
print("Visit http://ip:port for Web UI, e.g., http://127.0.0.1:7860")
fix_proxy(ipv6_enabled=gradio_ipv6)
create_web_demo().queue().launch(share=gradio_share, server_name=server_name, inbrowser=True)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/__init__.py | src/llamafactory/webui/__init__.py | python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false | |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/manager.py | src/llamafactory/webui/manager.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Generator
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from gradio.components import Component
class Manager:
r"""A class to manage all the gradio components in Web UI."""
def __init__(self) -> None:
self._id_to_elem: dict[str, Component] = {}
self._elem_to_id: dict[Component, str] = {}
def add_elems(self, tab_name: str, elem_dict: dict[str, "Component"]) -> None:
r"""Add elements to manager."""
for elem_name, elem in elem_dict.items():
elem_id = f"{tab_name}.{elem_name}"
self._id_to_elem[elem_id] = elem
self._elem_to_id[elem] = elem_id
def get_elem_list(self) -> list["Component"]:
r"""Return the list of all elements."""
return list(self._id_to_elem.values())
def get_elem_iter(self) -> Generator[tuple[str, "Component"], None, None]:
r"""Return an iterator over all elements with their names."""
for elem_id, elem in self._id_to_elem.items():
yield elem_id.split(".")[-1], elem
def get_elem_by_id(self, elem_id: str) -> "Component":
r"""Get element by id.
Example: top.lang, train.dataset
"""
return self._id_to_elem[elem_id]
def get_id_by_elem(self, elem: "Component") -> str:
r"""Get id by element."""
return self._elem_to_id[elem]
def get_base_elems(self) -> set["Component"]:
r"""Get the base elements that are commonly used."""
return {
self._id_to_elem["top.lang"],
self._id_to_elem["top.model_name"],
self._id_to_elem["top.model_path"],
self._id_to_elem["top.finetuning_type"],
self._id_to_elem["top.checkpoint_path"],
self._id_to_elem["top.quantization_bit"],
self._id_to_elem["top.quantization_method"],
self._id_to_elem["top.template"],
self._id_to_elem["top.rope_scaling"],
self._id_to_elem["top.booster"],
}
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/engine.py | src/llamafactory/webui/engine.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING, Any
from .chatter import WebChatModel
from .common import create_ds_config, get_time, load_config
from .locales import LOCALES
from .manager import Manager
from .runner import Runner
if TYPE_CHECKING:
from gradio.components import Component
class Engine:
r"""A general engine to control the behaviors of Web UI."""
def __init__(self, demo_mode: bool = False, pure_chat: bool = False) -> None:
self.demo_mode = demo_mode
self.pure_chat = pure_chat
self.manager = Manager()
self.runner = Runner(self.manager, demo_mode)
self.chatter = WebChatModel(self.manager, demo_mode, lazy_init=(not pure_chat))
if not demo_mode:
create_ds_config()
def _update_component(self, input_dict: dict[str, dict[str, Any]]) -> dict["Component", "Component"]:
r"""Update gradio components according to the (elem_id, properties) mapping."""
output_dict: dict[Component, Component] = {}
for elem_id, elem_attr in input_dict.items():
elem = self.manager.get_elem_by_id(elem_id)
output_dict[elem] = elem.__class__(**elem_attr)
return output_dict
def resume(self):
r"""Get the initial value of gradio components and restores training status if necessary."""
user_config = load_config() if not self.demo_mode else {} # do not use config in demo mode
lang = user_config.get("lang") or "en"
init_dict = {"top.lang": {"value": lang}, "infer.chat_box": {"visible": self.chatter.loaded}}
if not self.pure_chat:
current_time = get_time()
hub_name = user_config.get("hub_name") or "huggingface"
init_dict["top.hub_name"] = {"value": hub_name}
init_dict["train.current_time"] = {"value": current_time}
init_dict["train.output_dir"] = {"value": f"train_{current_time}"}
init_dict["train.config_path"] = {"value": f"{current_time}.yaml"}
init_dict["eval.output_dir"] = {"value": f"eval_{current_time}"}
init_dict["infer.mm_box"] = {"visible": False}
if user_config.get("last_model", None):
init_dict["top.model_name"] = {"value": user_config["last_model"]}
yield self._update_component(init_dict)
if self.runner.running and not self.demo_mode and not self.pure_chat:
yield {elem: elem.__class__(value=value) for elem, value in self.runner.running_data.items()}
if self.runner.do_train:
yield self._update_component({"train.resume_btn": {"value": True}})
else:
yield self._update_component({"eval.resume_btn": {"value": True}})
def change_lang(self, lang: str):
r"""Update the displayed language of gradio components."""
return {
elem: elem.__class__(**LOCALES[elem_name][lang])
for elem_name, elem in self.manager.get_elem_iter()
if elem_name in LOCALES
}
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/components/train.py | src/llamafactory/webui/components/train.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from transformers.trainer_utils import SchedulerType
from ...extras.constants import TRAINING_STAGES
from ...extras.misc import get_device_count
from ...extras.packages import is_gradio_available
from ..common import DEFAULT_DATA_DIR
from ..control import change_stage, list_checkpoints, list_config_paths, list_datasets, list_output_dirs
from .data import create_preview_box
if is_gradio_available():
import gradio as gr
if TYPE_CHECKING:
from gradio.components import Component
from ..engine import Engine
def create_train_tab(engine: "Engine") -> dict[str, "Component"]:
input_elems = engine.manager.get_base_elems()
elem_dict = dict()
with gr.Row():
stages = list(TRAINING_STAGES.keys())
training_stage = gr.Dropdown(choices=stages, value=stages[0], scale=1)
dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=1)
dataset = gr.Dropdown(multiselect=True, allow_custom_value=True, scale=4)
preview_elems = create_preview_box(dataset_dir, dataset)
input_elems.update({training_stage, dataset_dir, dataset})
elem_dict.update(dict(training_stage=training_stage, dataset_dir=dataset_dir, dataset=dataset, **preview_elems))
with gr.Row():
learning_rate = gr.Textbox(value="5e-5")
num_train_epochs = gr.Textbox(value="3.0")
max_grad_norm = gr.Textbox(value="1.0")
max_samples = gr.Textbox(value="100000")
compute_type = gr.Dropdown(choices=["bf16", "fp16", "fp32", "pure_bf16"], value="bf16")
input_elems.update({learning_rate, num_train_epochs, max_grad_norm, max_samples, compute_type})
elem_dict.update(
dict(
learning_rate=learning_rate,
num_train_epochs=num_train_epochs,
max_grad_norm=max_grad_norm,
max_samples=max_samples,
compute_type=compute_type,
)
)
with gr.Row():
cutoff_len = gr.Slider(minimum=4, maximum=131072, value=2048, step=1)
batch_size = gr.Slider(minimum=1, maximum=1024, value=2, step=1)
gradient_accumulation_steps = gr.Slider(minimum=1, maximum=1024, value=8, step=1)
val_size = gr.Slider(minimum=0, maximum=1, value=0, step=0.001)
lr_scheduler_type = gr.Dropdown(choices=[scheduler.value for scheduler in SchedulerType], value="cosine")
input_elems.update({cutoff_len, batch_size, gradient_accumulation_steps, val_size, lr_scheduler_type})
elem_dict.update(
dict(
cutoff_len=cutoff_len,
batch_size=batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
val_size=val_size,
lr_scheduler_type=lr_scheduler_type,
)
)
with gr.Accordion(open=False) as extra_tab:
with gr.Row():
logging_steps = gr.Slider(minimum=1, maximum=1000, value=5, step=5)
save_steps = gr.Slider(minimum=10, maximum=5000, value=100, step=10)
warmup_steps = gr.Slider(minimum=0, maximum=5000, value=0, step=1)
neftune_alpha = gr.Slider(minimum=0, maximum=10, value=0, step=0.1)
extra_args = gr.Textbox(value='{"optim": "adamw_torch"}')
with gr.Row():
with gr.Column():
packing = gr.Checkbox()
neat_packing = gr.Checkbox()
with gr.Column():
train_on_prompt = gr.Checkbox()
mask_history = gr.Checkbox()
with gr.Column():
resize_vocab = gr.Checkbox()
use_llama_pro = gr.Checkbox()
with gr.Column():
enable_thinking = gr.Checkbox(value=True)
report_to = gr.Dropdown(
choices=["none", "wandb", "mlflow", "neptune", "tensorboard", "all"],
value="none",
allow_custom_value=True,
)
input_elems.update(
{
logging_steps,
save_steps,
warmup_steps,
neftune_alpha,
extra_args,
packing,
neat_packing,
train_on_prompt,
mask_history,
resize_vocab,
use_llama_pro,
enable_thinking,
report_to,
}
)
elem_dict.update(
dict(
extra_tab=extra_tab,
logging_steps=logging_steps,
save_steps=save_steps,
warmup_steps=warmup_steps,
neftune_alpha=neftune_alpha,
extra_args=extra_args,
packing=packing,
neat_packing=neat_packing,
train_on_prompt=train_on_prompt,
mask_history=mask_history,
resize_vocab=resize_vocab,
use_llama_pro=use_llama_pro,
enable_thinking=enable_thinking,
report_to=report_to,
)
)
with gr.Accordion(open=False) as freeze_tab:
with gr.Row():
freeze_trainable_layers = gr.Slider(minimum=-128, maximum=128, value=2, step=1)
freeze_trainable_modules = gr.Textbox(value="all")
freeze_extra_modules = gr.Textbox()
input_elems.update({freeze_trainable_layers, freeze_trainable_modules, freeze_extra_modules})
elem_dict.update(
dict(
freeze_tab=freeze_tab,
freeze_trainable_layers=freeze_trainable_layers,
freeze_trainable_modules=freeze_trainable_modules,
freeze_extra_modules=freeze_extra_modules,
)
)
with gr.Accordion(open=False) as lora_tab:
with gr.Row():
lora_rank = gr.Slider(minimum=1, maximum=1024, value=8, step=1)
lora_alpha = gr.Slider(minimum=1, maximum=2048, value=16, step=1)
lora_dropout = gr.Slider(minimum=0, maximum=1, value=0, step=0.01)
loraplus_lr_ratio = gr.Slider(minimum=0, maximum=64, value=0, step=0.01)
create_new_adapter = gr.Checkbox()
with gr.Row():
use_rslora = gr.Checkbox()
use_dora = gr.Checkbox()
use_pissa = gr.Checkbox()
lora_target = gr.Textbox(scale=2)
additional_target = gr.Textbox(scale=2)
input_elems.update(
{
lora_rank,
lora_alpha,
lora_dropout,
loraplus_lr_ratio,
create_new_adapter,
use_rslora,
use_dora,
use_pissa,
lora_target,
additional_target,
}
)
elem_dict.update(
dict(
lora_tab=lora_tab,
lora_rank=lora_rank,
lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
loraplus_lr_ratio=loraplus_lr_ratio,
create_new_adapter=create_new_adapter,
use_rslora=use_rslora,
use_dora=use_dora,
use_pissa=use_pissa,
lora_target=lora_target,
additional_target=additional_target,
)
)
with gr.Accordion(open=False) as rlhf_tab:
with gr.Row():
pref_beta = gr.Slider(minimum=0, maximum=1, value=0.1, step=0.01)
pref_ftx = gr.Slider(minimum=0, maximum=10, value=0, step=0.01)
pref_loss = gr.Dropdown(choices=["sigmoid", "hinge", "ipo", "kto_pair", "orpo", "simpo"], value="sigmoid")
reward_model = gr.Dropdown(multiselect=True, allow_custom_value=True)
with gr.Column():
ppo_score_norm = gr.Checkbox()
ppo_whiten_rewards = gr.Checkbox()
input_elems.update({pref_beta, pref_ftx, pref_loss, reward_model, ppo_score_norm, ppo_whiten_rewards})
elem_dict.update(
dict(
rlhf_tab=rlhf_tab,
pref_beta=pref_beta,
pref_ftx=pref_ftx,
pref_loss=pref_loss,
reward_model=reward_model,
ppo_score_norm=ppo_score_norm,
ppo_whiten_rewards=ppo_whiten_rewards,
)
)
with gr.Accordion(open=False) as mm_tab:
with gr.Row():
freeze_vision_tower = gr.Checkbox(value=True)
freeze_multi_modal_projector = gr.Checkbox(value=True)
freeze_language_model = gr.Checkbox(value=False)
with gr.Row():
image_max_pixels = gr.Textbox(value="768*768")
image_min_pixels = gr.Textbox(value="32*32")
video_max_pixels = gr.Textbox(value="256*256")
video_min_pixels = gr.Textbox(value="16*16")
input_elems.update(
{
freeze_vision_tower,
freeze_multi_modal_projector,
freeze_language_model,
image_max_pixels,
image_min_pixels,
video_max_pixels,
video_min_pixels,
}
)
elem_dict.update(
dict(
mm_tab=mm_tab,
freeze_vision_tower=freeze_vision_tower,
freeze_multi_modal_projector=freeze_multi_modal_projector,
freeze_language_model=freeze_language_model,
image_max_pixels=image_max_pixels,
image_min_pixels=image_min_pixels,
video_max_pixels=video_max_pixels,
video_min_pixels=video_min_pixels,
)
)
with gr.Accordion(open=False) as galore_tab:
with gr.Row():
use_galore = gr.Checkbox()
galore_rank = gr.Slider(minimum=1, maximum=1024, value=16, step=1)
galore_update_interval = gr.Slider(minimum=1, maximum=2048, value=200, step=1)
galore_scale = gr.Slider(minimum=0, maximum=100, value=2.0, step=0.1)
galore_target = gr.Textbox(value="all")
input_elems.update({use_galore, galore_rank, galore_update_interval, galore_scale, galore_target})
elem_dict.update(
dict(
galore_tab=galore_tab,
use_galore=use_galore,
galore_rank=galore_rank,
galore_update_interval=galore_update_interval,
galore_scale=galore_scale,
galore_target=galore_target,
)
)
with gr.Accordion(open=False) as apollo_tab:
with gr.Row():
use_apollo = gr.Checkbox()
apollo_rank = gr.Slider(minimum=1, maximum=1024, value=16, step=1)
apollo_update_interval = gr.Slider(minimum=1, maximum=2048, value=200, step=1)
apollo_scale = gr.Slider(minimum=0, maximum=100, value=32.0, step=0.1)
apollo_target = gr.Textbox(value="all")
input_elems.update({use_apollo, apollo_rank, apollo_update_interval, apollo_scale, apollo_target})
elem_dict.update(
dict(
apollo_tab=apollo_tab,
use_apollo=use_apollo,
apollo_rank=apollo_rank,
apollo_update_interval=apollo_update_interval,
apollo_scale=apollo_scale,
apollo_target=apollo_target,
)
)
with gr.Accordion(open=False) as badam_tab:
with gr.Row():
use_badam = gr.Checkbox()
badam_mode = gr.Dropdown(choices=["layer", "ratio"], value="layer")
badam_switch_mode = gr.Dropdown(choices=["ascending", "descending", "random", "fixed"], value="ascending")
badam_switch_interval = gr.Slider(minimum=1, maximum=1024, value=50, step=1)
badam_update_ratio = gr.Slider(minimum=0, maximum=1, value=0.05, step=0.01)
input_elems.update({use_badam, badam_mode, badam_switch_mode, badam_switch_interval, badam_update_ratio})
elem_dict.update(
dict(
badam_tab=badam_tab,
use_badam=use_badam,
badam_mode=badam_mode,
badam_switch_mode=badam_switch_mode,
badam_switch_interval=badam_switch_interval,
badam_update_ratio=badam_update_ratio,
)
)
with gr.Accordion(open=False) as swanlab_tab:
with gr.Row():
use_swanlab = gr.Checkbox()
swanlab_project = gr.Textbox(value="llamafactory")
swanlab_run_name = gr.Textbox()
swanlab_workspace = gr.Textbox()
swanlab_api_key = gr.Textbox()
swanlab_mode = gr.Dropdown(choices=["cloud", "local"], value="cloud")
swanlab_link = gr.Markdown(visible=False)
input_elems.update(
{
use_swanlab,
swanlab_project,
swanlab_run_name,
swanlab_workspace,
swanlab_api_key,
swanlab_mode,
swanlab_link,
}
)
elem_dict.update(
dict(
swanlab_tab=swanlab_tab,
use_swanlab=use_swanlab,
swanlab_project=swanlab_project,
swanlab_run_name=swanlab_run_name,
swanlab_workspace=swanlab_workspace,
swanlab_api_key=swanlab_api_key,
swanlab_mode=swanlab_mode,
swanlab_link=swanlab_link,
)
)
with gr.Row():
cmd_preview_btn = gr.Button()
arg_save_btn = gr.Button()
arg_load_btn = gr.Button()
start_btn = gr.Button(variant="primary")
stop_btn = gr.Button(variant="stop")
with gr.Row():
with gr.Column(scale=3):
with gr.Row():
current_time = gr.Textbox(visible=False, interactive=False)
output_dir = gr.Dropdown(allow_custom_value=True)
config_path = gr.Dropdown(allow_custom_value=True)
with gr.Row():
device_count = gr.Textbox(value=str(get_device_count() or 1), interactive=False)
ds_stage = gr.Dropdown(choices=["none", "2", "3"], value="none")
ds_offload = gr.Checkbox()
with gr.Row():
resume_btn = gr.Checkbox(visible=False, interactive=False)
progress_bar = gr.Slider(visible=False, interactive=False)
with gr.Row():
output_box = gr.Markdown()
with gr.Column(scale=1):
loss_viewer = gr.Plot()
input_elems.update({output_dir, config_path, ds_stage, ds_offload})
elem_dict.update(
dict(
cmd_preview_btn=cmd_preview_btn,
arg_save_btn=arg_save_btn,
arg_load_btn=arg_load_btn,
start_btn=start_btn,
stop_btn=stop_btn,
current_time=current_time,
output_dir=output_dir,
config_path=config_path,
device_count=device_count,
ds_stage=ds_stage,
ds_offload=ds_offload,
resume_btn=resume_btn,
progress_bar=progress_bar,
output_box=output_box,
loss_viewer=loss_viewer,
)
)
output_elems = [output_box, progress_bar, loss_viewer, swanlab_link]
cmd_preview_btn.click(engine.runner.preview_train, input_elems, output_elems, concurrency_limit=None)
start_btn.click(engine.runner.run_train, input_elems, output_elems)
stop_btn.click(engine.runner.set_abort)
resume_btn.change(engine.runner.monitor, outputs=output_elems, concurrency_limit=None)
lang = engine.manager.get_elem_by_id("top.lang")
model_name: gr.Dropdown = engine.manager.get_elem_by_id("top.model_name")
finetuning_type: gr.Dropdown = engine.manager.get_elem_by_id("top.finetuning_type")
arg_save_btn.click(engine.runner.save_args, input_elems, output_elems, concurrency_limit=None)
arg_load_btn.click(
engine.runner.load_args, [lang, config_path], list(input_elems) + [output_box], concurrency_limit=None
)
dataset.focus(list_datasets, [dataset_dir, training_stage], [dataset], queue=False)
training_stage.change(change_stage, [training_stage], [dataset, packing], queue=False)
reward_model.focus(list_checkpoints, [model_name, finetuning_type], [reward_model], queue=False)
model_name.change(list_output_dirs, [model_name, finetuning_type, current_time], [output_dir], queue=False)
finetuning_type.change(list_output_dirs, [model_name, finetuning_type, current_time], [output_dir], queue=False)
output_dir.change(
list_output_dirs, [model_name, finetuning_type, current_time], [output_dir], concurrency_limit=None
)
output_dir.input(
engine.runner.check_output_dir,
[lang, model_name, finetuning_type, output_dir],
list(input_elems) + [output_box],
concurrency_limit=None,
)
config_path.change(list_config_paths, [current_time], [config_path], queue=False)
return elem_dict
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/components/export.py | src/llamafactory/webui/components/export.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
from collections.abc import Generator
from typing import TYPE_CHECKING
from ...extras.constants import PEFT_METHODS
from ...extras.misc import torch_gc
from ...extras.packages import is_gradio_available
from ...train.tuner import export_model
from ..common import get_save_dir, load_config
from ..locales import ALERTS
if is_gradio_available():
import gradio as gr
if TYPE_CHECKING:
from gradio.components import Component
from ..engine import Engine
GPTQ_BITS = ["8", "4", "3", "2"]
def can_quantize(checkpoint_path: str | list[str]) -> "gr.Dropdown":
if isinstance(checkpoint_path, list) and len(checkpoint_path) != 0:
return gr.Dropdown(value="none", interactive=False)
else:
return gr.Dropdown(interactive=True)
def save_model(
lang: str,
model_name: str,
model_path: str,
finetuning_type: str,
checkpoint_path: str | list[str],
template: str,
export_size: int,
export_quantization_bit: str,
export_quantization_dataset: str,
export_device: str,
export_legacy_format: bool,
export_dir: str,
export_hub_model_id: str,
extra_args: str,
) -> Generator[str, None, None]:
user_config = load_config()
error = ""
if not model_name:
error = ALERTS["err_no_model"][lang]
elif not model_path:
error = ALERTS["err_no_path"][lang]
elif not export_dir:
error = ALERTS["err_no_export_dir"][lang]
elif export_quantization_bit in GPTQ_BITS and not export_quantization_dataset:
error = ALERTS["err_no_dataset"][lang]
elif export_quantization_bit not in GPTQ_BITS and not checkpoint_path:
error = ALERTS["err_no_adapter"][lang]
elif export_quantization_bit in GPTQ_BITS and checkpoint_path and isinstance(checkpoint_path, list):
error = ALERTS["err_gptq_lora"][lang]
try:
json.loads(extra_args)
except json.JSONDecodeError:
error = ALERTS["err_json_schema"][lang]
if error:
gr.Warning(error)
yield error
return
args = dict(
model_name_or_path=model_path,
cache_dir=user_config.get("cache_dir", None),
finetuning_type=finetuning_type,
template=template,
export_dir=export_dir,
export_hub_model_id=export_hub_model_id or None,
export_size=export_size,
export_quantization_bit=int(export_quantization_bit) if export_quantization_bit in GPTQ_BITS else None,
export_quantization_dataset=export_quantization_dataset,
export_device=export_device,
export_legacy_format=export_legacy_format,
trust_remote_code=True,
)
args.update(json.loads(extra_args))
if checkpoint_path:
if finetuning_type in PEFT_METHODS: # list
args["adapter_name_or_path"] = ",".join(
[get_save_dir(model_name, finetuning_type, adapter) for adapter in checkpoint_path]
)
else: # str
args["model_name_or_path"] = get_save_dir(model_name, finetuning_type, checkpoint_path)
yield ALERTS["info_exporting"][lang]
export_model(args)
torch_gc()
yield ALERTS["info_exported"][lang]
def create_export_tab(engine: "Engine") -> dict[str, "Component"]:
with gr.Row():
export_size = gr.Slider(minimum=1, maximum=100, value=5, step=1)
export_quantization_bit = gr.Dropdown(choices=["none"] + GPTQ_BITS, value="none")
export_quantization_dataset = gr.Textbox(value="data/c4_demo.jsonl")
export_device = gr.Radio(choices=["cpu", "auto"], value="cpu")
export_legacy_format = gr.Checkbox()
with gr.Row():
export_dir = gr.Textbox()
export_hub_model_id = gr.Textbox()
extra_args = gr.Textbox(value="{}")
checkpoint_path: gr.Dropdown = engine.manager.get_elem_by_id("top.checkpoint_path")
checkpoint_path.change(can_quantize, [checkpoint_path], [export_quantization_bit], queue=False)
export_btn = gr.Button()
info_box = gr.Textbox(show_label=False, interactive=False)
export_btn.click(
save_model,
[
engine.manager.get_elem_by_id("top.lang"),
engine.manager.get_elem_by_id("top.model_name"),
engine.manager.get_elem_by_id("top.model_path"),
engine.manager.get_elem_by_id("top.finetuning_type"),
engine.manager.get_elem_by_id("top.checkpoint_path"),
engine.manager.get_elem_by_id("top.template"),
export_size,
export_quantization_bit,
export_quantization_dataset,
export_device,
export_legacy_format,
export_dir,
export_hub_model_id,
extra_args,
],
[info_box],
)
return dict(
export_size=export_size,
export_quantization_bit=export_quantization_bit,
export_quantization_dataset=export_quantization_dataset,
export_device=export_device,
export_legacy_format=export_legacy_format,
export_dir=export_dir,
export_hub_model_id=export_hub_model_id,
extra_args=extra_args,
export_btn=export_btn,
info_box=info_box,
)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/components/infer.py | src/llamafactory/webui/components/infer.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...extras.packages import is_gradio_available
from ..common import is_multimodal
from .chatbot import create_chat_box
if is_gradio_available():
import gradio as gr
if TYPE_CHECKING:
from gradio.components import Component
from ..engine import Engine
def create_infer_tab(engine: "Engine") -> dict[str, "Component"]:
input_elems = engine.manager.get_base_elems()
elem_dict = dict()
with gr.Row():
infer_backend = gr.Dropdown(choices=["huggingface", "vllm", "sglang"], value="huggingface")
infer_dtype = gr.Dropdown(choices=["auto", "float16", "bfloat16", "float32"], value="auto")
extra_args = gr.Textbox(value='{"vllm_enforce_eager": true}')
with gr.Row():
load_btn = gr.Button()
unload_btn = gr.Button()
info_box = gr.Textbox(show_label=False, interactive=False)
input_elems.update({infer_backend, infer_dtype, extra_args})
elem_dict.update(
dict(
infer_backend=infer_backend,
infer_dtype=infer_dtype,
extra_args=extra_args,
load_btn=load_btn,
unload_btn=unload_btn,
info_box=info_box,
)
)
chatbot, messages, chat_elems = create_chat_box(engine, visible=False)
elem_dict.update(chat_elems)
load_btn.click(engine.chatter.load_model, input_elems, [info_box]).then(
lambda: gr.Column(visible=engine.chatter.loaded), outputs=[chat_elems["chat_box"]]
)
unload_btn.click(engine.chatter.unload_model, input_elems, [info_box]).then(
lambda: ([], []), outputs=[chatbot, messages]
).then(lambda: gr.Column(visible=engine.chatter.loaded), outputs=[chat_elems["chat_box"]])
engine.manager.get_elem_by_id("top.model_name").change(
lambda model_name: gr.Column(visible=is_multimodal(model_name)),
[engine.manager.get_elem_by_id("top.model_name")],
[chat_elems["mm_box"]],
)
return elem_dict
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/components/eval.py | src/llamafactory/webui/components/eval.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...extras.packages import is_gradio_available
from ..common import DEFAULT_DATA_DIR
from ..control import list_datasets
from .data import create_preview_box
if is_gradio_available():
import gradio as gr
if TYPE_CHECKING:
from gradio.components import Component
from ..engine import Engine
def create_eval_tab(engine: "Engine") -> dict[str, "Component"]:
input_elems = engine.manager.get_base_elems()
elem_dict = dict()
with gr.Row():
dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
dataset = gr.Dropdown(multiselect=True, allow_custom_value=True, scale=4)
preview_elems = create_preview_box(dataset_dir, dataset)
input_elems.update({dataset_dir, dataset})
elem_dict.update(dict(dataset_dir=dataset_dir, dataset=dataset, **preview_elems))
with gr.Row():
cutoff_len = gr.Slider(minimum=4, maximum=131072, value=1024, step=1)
max_samples = gr.Textbox(value="100000")
batch_size = gr.Slider(minimum=1, maximum=1024, value=2, step=1)
predict = gr.Checkbox(value=True)
input_elems.update({cutoff_len, max_samples, batch_size, predict})
elem_dict.update(dict(cutoff_len=cutoff_len, max_samples=max_samples, batch_size=batch_size, predict=predict))
with gr.Row():
max_new_tokens = gr.Slider(minimum=8, maximum=4096, value=512, step=1)
top_p = gr.Slider(minimum=0.01, maximum=1, value=0.7, step=0.01)
temperature = gr.Slider(minimum=0.01, maximum=1.5, value=0.95, step=0.01)
output_dir = gr.Textbox()
input_elems.update({max_new_tokens, top_p, temperature, output_dir})
elem_dict.update(dict(max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature, output_dir=output_dir))
with gr.Row():
cmd_preview_btn = gr.Button()
start_btn = gr.Button(variant="primary")
stop_btn = gr.Button(variant="stop")
with gr.Row():
resume_btn = gr.Checkbox(visible=False, interactive=False)
progress_bar = gr.Slider(visible=False, interactive=False)
with gr.Row():
output_box = gr.Markdown()
elem_dict.update(
dict(
cmd_preview_btn=cmd_preview_btn,
start_btn=start_btn,
stop_btn=stop_btn,
resume_btn=resume_btn,
progress_bar=progress_bar,
output_box=output_box,
)
)
output_elems = [output_box, progress_bar]
cmd_preview_btn.click(engine.runner.preview_eval, input_elems, output_elems, concurrency_limit=None)
start_btn.click(engine.runner.run_eval, input_elems, output_elems)
stop_btn.click(engine.runner.set_abort)
resume_btn.change(engine.runner.monitor, outputs=output_elems, concurrency_limit=None)
dataset.focus(list_datasets, [dataset_dir], [dataset], queue=False)
return elem_dict
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/components/footer.py | src/llamafactory/webui/components/footer.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...extras.misc import get_current_memory
from ...extras.packages import is_gradio_available
if is_gradio_available():
import gradio as gr
if TYPE_CHECKING:
from gradio.components import Component
def get_device_memory() -> "gr.Slider":
free, total = get_current_memory()
if total != -1:
used = round((total - free) / (1024**3), 2)
total = round(total / (1024**3), 2)
return gr.Slider(minimum=0, maximum=total, value=used, step=0.01, visible=True)
else:
return gr.Slider(visible=False)
def create_footer() -> dict[str, "Component"]:
with gr.Row():
device_memory = gr.Slider(visible=False, interactive=False)
timer = gr.Timer(value=5)
timer.tick(get_device_memory, outputs=[device_memory], queue=False)
return dict(device_memory=device_memory)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/components/chatbot.py | src/llamafactory/webui/components/chatbot.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import json
from typing import TYPE_CHECKING
from ...data import Role
from ...extras.packages import is_gradio_available
from ..locales import ALERTS
if is_gradio_available():
import gradio as gr
if TYPE_CHECKING:
from gradio.components import Component
from ..engine import Engine
def check_json_schema(text: str, lang: str) -> None:
r"""Check if the json schema is valid."""
try:
tools = json.loads(text)
if tools:
assert isinstance(tools, list)
for tool in tools:
if "name" not in tool:
raise NotImplementedError("Name not found.")
except NotImplementedError:
gr.Warning(ALERTS["err_tool_name"][lang])
except Exception:
gr.Warning(ALERTS["err_json_schema"][lang])
def create_chat_box(
engine: "Engine", visible: bool = False
) -> tuple["Component", "Component", dict[str, "Component"]]:
lang = engine.manager.get_elem_by_id("top.lang")
with gr.Column(visible=visible) as chat_box:
kwargs = {}
if "show_copy_button" in inspect.signature(gr.Chatbot.__init__).parameters:
kwargs["show_copy_button"] = True
if "resizable" in inspect.signature(gr.Chatbot.__init__).parameters:
kwargs["resizable"] = True
chatbot = gr.Chatbot(type="messages", **kwargs)
messages = gr.State([])
with gr.Row():
with gr.Column(scale=4):
with gr.Row():
with gr.Column():
role = gr.Dropdown(choices=[Role.USER.value, Role.OBSERVATION.value], value=Role.USER.value)
system = gr.Textbox(show_label=False)
tools = gr.Textbox(show_label=False, lines=3)
with gr.Column() as mm_box:
with gr.Tab("Image"):
image = gr.Image(type="pil")
with gr.Tab("Video"):
video = gr.Video()
with gr.Tab("Audio"):
audio = gr.Audio(type="filepath")
query = gr.Textbox(show_label=False, lines=8)
submit_btn = gr.Button(variant="primary")
with gr.Column(scale=1):
max_new_tokens = gr.Slider(minimum=8, maximum=8192, value=1024, step=1)
top_p = gr.Slider(minimum=0.01, maximum=1.0, value=0.7, step=0.01)
temperature = gr.Slider(minimum=0.01, maximum=1.5, value=0.95, step=0.01)
skip_special_tokens = gr.Checkbox(value=True)
escape_html = gr.Checkbox(value=True)
enable_thinking = gr.Checkbox(value=True)
clear_btn = gr.Button()
tools.input(check_json_schema, inputs=[tools, engine.manager.get_elem_by_id("top.lang")])
submit_btn.click(
engine.chatter.append,
[chatbot, messages, role, query, escape_html],
[chatbot, messages, query],
).then(
engine.chatter.stream,
[
chatbot,
messages,
lang,
system,
tools,
image,
video,
audio,
max_new_tokens,
top_p,
temperature,
skip_special_tokens,
escape_html,
enable_thinking,
],
[chatbot, messages],
)
clear_btn.click(lambda: ([], []), outputs=[chatbot, messages])
return (
chatbot,
messages,
dict(
chat_box=chat_box,
role=role,
system=system,
tools=tools,
mm_box=mm_box,
image=image,
video=video,
audio=audio,
query=query,
submit_btn=submit_btn,
max_new_tokens=max_new_tokens,
top_p=top_p,
temperature=temperature,
skip_special_tokens=skip_special_tokens,
escape_html=escape_html,
enable_thinking=enable_thinking,
clear_btn=clear_btn,
),
)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/components/__init__.py | src/llamafactory/webui/components/__init__.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .chatbot import create_chat_box
from .eval import create_eval_tab
from .export import create_export_tab
from .footer import create_footer
from .infer import create_infer_tab
from .top import create_top
from .train import create_train_tab
__all__ = [
"create_chat_box",
"create_eval_tab",
"create_export_tab",
"create_footer",
"create_infer_tab",
"create_top",
"create_train_tab",
]
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/components/top.py | src/llamafactory/webui/components/top.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...data import TEMPLATES
from ...extras.constants import METHODS, SUPPORTED_MODELS
from ...extras.misc import use_modelscope, use_openmind
from ...extras.packages import is_gradio_available
from ..common import save_config
from ..control import can_quantize, can_quantize_to, check_template, get_model_info, list_checkpoints, switch_hub
if is_gradio_available():
import gradio as gr
if TYPE_CHECKING:
from gradio.components import Component
def create_top() -> dict[str, "Component"]:
with gr.Row():
lang = gr.Dropdown(choices=["en", "ru", "zh", "ko", "ja"], value=None, scale=1)
available_models = list(SUPPORTED_MODELS.keys()) + ["Custom"]
model_name = gr.Dropdown(choices=available_models, value=None, scale=2)
model_path = gr.Textbox(scale=2)
default_hub = "modelscope" if use_modelscope() else "openmind" if use_openmind() else "huggingface"
hub_name = gr.Dropdown(choices=["huggingface", "modelscope", "openmind"], value=default_hub, scale=2)
with gr.Row():
finetuning_type = gr.Dropdown(choices=METHODS, value="lora", scale=1)
checkpoint_path = gr.Dropdown(multiselect=True, allow_custom_value=True, scale=6)
with gr.Row():
quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none", allow_custom_value=True)
quantization_method = gr.Dropdown(choices=["bnb", "hqq", "eetq"], value="bnb")
template = gr.Dropdown(choices=list(TEMPLATES.keys()), value="default")
rope_scaling = gr.Dropdown(choices=["none", "linear", "dynamic", "yarn", "llama3"], value="none")
booster = gr.Dropdown(choices=["auto", "flashattn2", "unsloth", "liger_kernel"], value="auto")
model_name.change(get_model_info, [model_name], [model_path, template], queue=False).then(
list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False
).then(check_template, [lang, template])
model_name.input(save_config, inputs=[lang, hub_name, model_name], queue=False)
model_path.input(save_config, inputs=[lang, hub_name, model_name, model_path], queue=False)
finetuning_type.change(can_quantize, [finetuning_type], [quantization_bit], queue=False).then(
list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False
)
checkpoint_path.focus(list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False)
quantization_method.change(can_quantize_to, [quantization_method], [quantization_bit], queue=False)
hub_name.change(switch_hub, inputs=[hub_name], queue=False).then(
get_model_info, [model_name], [model_path, template], queue=False
).then(list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False).then(
check_template, [lang, template]
)
hub_name.input(save_config, inputs=[lang, hub_name], queue=False)
return dict(
lang=lang,
model_name=model_name,
model_path=model_path,
hub_name=hub_name,
finetuning_type=finetuning_type,
checkpoint_path=checkpoint_path,
quantization_bit=quantization_bit,
quantization_method=quantization_method,
template=template,
rope_scaling=rope_scaling,
booster=booster,
)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/src/llamafactory/webui/components/data.py | src/llamafactory/webui/components/data.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os
from typing import TYPE_CHECKING, Any
from ...extras.constants import DATA_CONFIG
from ...extras.packages import is_gradio_available
if is_gradio_available():
import gradio as gr
if TYPE_CHECKING:
from gradio.components import Component
PAGE_SIZE = 2
def prev_page(page_index: int) -> int:
return page_index - 1 if page_index > 0 else page_index
def next_page(page_index: int, total_num: int) -> int:
return page_index + 1 if (page_index + 1) * PAGE_SIZE < total_num else page_index
def can_preview(dataset_dir: str, dataset: list) -> "gr.Button":
r"""Check if the dataset is a local dataset."""
try:
with open(os.path.join(dataset_dir, DATA_CONFIG), encoding="utf-8") as f:
dataset_info = json.load(f)
except Exception:
return gr.Button(interactive=False)
if len(dataset) == 0 or "file_name" not in dataset_info[dataset[0]]:
return gr.Button(interactive=False)
data_path = os.path.join(dataset_dir, dataset_info[dataset[0]]["file_name"])
if os.path.isfile(data_path) or (os.path.isdir(data_path) and os.listdir(data_path)):
return gr.Button(interactive=True)
else:
return gr.Button(interactive=False)
def _load_data_file(file_path: str) -> list[Any]:
with open(file_path, encoding="utf-8") as f:
if file_path.endswith(".json"):
return json.load(f)
elif file_path.endswith(".jsonl"):
return [json.loads(line) for line in f]
else:
return list(f)
def get_preview(dataset_dir: str, dataset: list, page_index: int) -> tuple[int, list, "gr.Column"]:
r"""Get the preview samples from the dataset."""
with open(os.path.join(dataset_dir, DATA_CONFIG), encoding="utf-8") as f:
dataset_info = json.load(f)
data_path = os.path.join(dataset_dir, dataset_info[dataset[0]]["file_name"])
if os.path.isfile(data_path):
data = _load_data_file(data_path)
else:
data = []
for file_name in os.listdir(data_path):
data.extend(_load_data_file(os.path.join(data_path, file_name)))
return len(data), data[PAGE_SIZE * page_index : PAGE_SIZE * (page_index + 1)], gr.Column(visible=True)
def create_preview_box(dataset_dir: "gr.Textbox", dataset: "gr.Dropdown") -> dict[str, "Component"]:
data_preview_btn = gr.Button(interactive=False, scale=1)
with gr.Column(visible=False, elem_classes="modal-box") as preview_box:
with gr.Row():
preview_count = gr.Number(value=0, interactive=False, precision=0)
page_index = gr.Number(value=0, interactive=False, precision=0)
with gr.Row():
prev_btn = gr.Button()
next_btn = gr.Button()
close_btn = gr.Button()
with gr.Row():
preview_samples = gr.JSON()
dataset.change(can_preview, [dataset_dir, dataset], [data_preview_btn], queue=False).then(
lambda: 0, outputs=[page_index], queue=False
)
data_preview_btn.click(
get_preview, [dataset_dir, dataset, page_index], [preview_count, preview_samples, preview_box], queue=False
)
prev_btn.click(prev_page, [page_index], [page_index], queue=False).then(
get_preview, [dataset_dir, dataset, page_index], [preview_count, preview_samples, preview_box], queue=False
)
next_btn.click(next_page, [page_index, preview_count], [page_index], queue=False).then(
get_preview, [dataset_dir, dataset, page_index], [preview_count, preview_samples, preview_box], queue=False
)
close_btn.click(lambda: gr.Column(visible=False), outputs=[preview_box], queue=False)
return dict(
data_preview_btn=data_preview_btn,
preview_count=preview_count,
page_index=page_index,
prev_btn=prev_btn,
next_btn=next_btn,
close_btn=close_btn,
preview_samples=preview_samples,
)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/check_license.py | tests/check_license.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
from pathlib import Path
KEYWORDS = ("Copyright", "2025", "LlamaFactory")
def main():
path_list: list[Path] = []
for check_dir in sys.argv[1:]:
path_list.extend(Path(check_dir).glob("**/*.py"))
for path in path_list:
with open(path.absolute(), encoding="utf-8") as f:
file_content = f.read().strip().split("\n")
if not file_content[0]:
continue
print(f"Check license: {path}")
assert all(keyword in file_content[0] for keyword in KEYWORDS), f"File {path} does not contain license."
if __name__ == "__main__":
main()
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/conftest.py | tests/conftest.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""LlamaFactory test configuration.
Contains shared fixtures, pytest configuration, and custom markers.
"""
import os
from typing import Optional
import pytest
import torch
import torch.distributed as dist
from pytest import Config, FixtureRequest, Item, MonkeyPatch
from llamafactory.extras.misc import get_current_device, get_device_count, is_env_enabled
from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.train.test_utils import patch_valuehead_model
CURRENT_DEVICE = get_current_device().type
def pytest_configure(config: Config):
"""Register custom pytest markers."""
config.addinivalue_line(
"markers",
"slow: marks tests as slow (deselect with '-m \"not slow\"' or set RUN_SLOW=1 to run)",
)
config.addinivalue_line(
"markers",
"runs_on: test requires specific device type, e.g., @pytest.mark.runs_on(['cuda'])",
)
config.addinivalue_line(
"markers",
"require_distributed(num_devices): allow multi-device execution (default: 2)",
)
def _handle_runs_on(items: list[Item]):
"""Skip tests on specified device TYPES (cpu/cuda/npu)."""
for item in items:
marker = item.get_closest_marker("runs_on")
if not marker:
continue
devices = marker.args[0]
if isinstance(devices, str):
devices = [devices]
if CURRENT_DEVICE not in devices:
item.add_marker(pytest.mark.skip(reason=f"test requires one of {devices} (current: {CURRENT_DEVICE})"))
def _handle_slow_tests(items: list[Item]):
"""Skip slow tests unless RUN_SLOW is enabled."""
if not is_env_enabled("RUN_SLOW"):
skip_slow = pytest.mark.skip(reason="slow test (set RUN_SLOW=1 to run)")
for item in items:
if "slow" in item.keywords:
item.add_marker(skip_slow)
def _get_visible_devices_env() -> Optional[str]:
"""Return device visibility env var name."""
if CURRENT_DEVICE == "cuda":
return "CUDA_VISIBLE_DEVICES"
elif CURRENT_DEVICE == "npu":
return "ASCEND_RT_VISIBLE_DEVICES"
else:
return None
def _handle_device_visibility(items: list[Item]):
"""Handle device visibility based on test markers."""
env_key = _get_visible_devices_env()
if env_key is None or CURRENT_DEVICE in ("cpu", "mps"):
return
# Parse visible devices
visible_devices_env = os.environ.get(env_key)
if visible_devices_env is None:
available = get_device_count()
else:
visible_devices = [v for v in visible_devices_env.split(",") if v != ""]
available = len(visible_devices)
for item in items:
marker = item.get_closest_marker("require_distributed")
if not marker:
continue
required = marker.args[0] if marker.args else 2
if available < required:
item.add_marker(pytest.mark.skip(reason=f"test requires {required} devices, but only {available} visible"))
def pytest_collection_modifyitems(config: Config, items: list[Item]):
"""Modify test collection based on markers and environment."""
# Handle version compatibility (from HEAD)
skip_bc = pytest.mark.skip(reason="Skip backward compatibility tests")
for item in items:
if "tests_v1" in str(item.fspath) and not is_transformers_version_greater_than("4.57.0"):
item.add_marker(skip_bc)
_handle_slow_tests(items)
_handle_runs_on(items)
_handle_device_visibility(items)
@pytest.fixture(autouse=True)
def _cleanup_distributed_state():
"""Cleanup distributed state after each test."""
yield
if dist.is_initialized():
dist.destroy_process_group()
@pytest.fixture(autouse=True)
def _manage_distributed_env(request: FixtureRequest, monkeypatch: MonkeyPatch) -> None:
"""Set environment variables for distributed tests if specific devices are requested."""
env_key = _get_visible_devices_env()
if not env_key:
return
# Save old environment for logic checks, monkeypatch handles restoration
old_value = os.environ.get(env_key)
marker = request.node.get_closest_marker("require_distributed")
if marker: # distributed test
required = marker.args[0] if marker.args else 2
specific_devices = marker.args[1] if len(marker.args) > 1 else None
if specific_devices:
devices_str = ",".join(map(str, specific_devices))
else:
devices_str = ",".join(str(i) for i in range(required))
monkeypatch.setenv(env_key, devices_str)
else: # non-distributed test
if old_value:
visible_devices = [v for v in old_value.split(",") if v != ""]
monkeypatch.setenv(env_key, visible_devices[0] if visible_devices else "0")
else:
monkeypatch.setenv(env_key, "0")
if CURRENT_DEVICE == "cuda":
monkeypatch.setattr(torch.cuda, "device_count", lambda: 1)
elif CURRENT_DEVICE == "npu":
monkeypatch.setattr(torch.npu, "device_count", lambda: 1)
@pytest.fixture
def fix_valuehead_cpu_loading():
"""Fix valuehead model loading."""
patch_valuehead_model()
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/train/test_sft_trainer.py | tests/train/test_sft_trainer.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from dataclasses import dataclass, field
from typing import Any
import pytest
from transformers import DataCollatorWithPadding
from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
from llamafactory.hparams import get_train_args
from llamafactory.model import load_model, load_tokenizer
from llamafactory.train.sft.trainer import CustomSeq2SeqTrainer
DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TRAIN_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"stage": "sft",
"do_train": True,
"finetuning_type": "lora",
"dataset": "llamafactory/tiny-supervised-dataset",
"dataset_dir": "ONLINE",
"template": "llama3",
"cutoff_len": 1024,
"overwrite_output_dir": True,
"per_device_train_batch_size": 1,
"max_steps": 1,
"report_to": "none",
}
@dataclass
class DataCollatorWithVerbose(DataCollatorWithPadding):
verbose_list: list[dict[str, Any]] = field(default_factory=list)
def __call__(self, features: list[dict[str, Any]]) -> dict[str, Any]:
features = [
{k: v for k, v in feature.items() if k in ["input_ids", "attention_mask", "labels"]}
for feature in features
]
self.verbose_list.extend(features)
batch = super().__call__(features)
return {k: v[:, :1] for k, v in batch.items()} # truncate input length
@pytest.mark.parametrize("disable_shuffling", [False, True])
def test_shuffle(disable_shuffling: bool):
model_args, data_args, training_args, finetuning_args, _ = get_train_args(
{
"output_dir": os.path.join("output", f"shuffle{str(disable_shuffling).lower()}"),
"disable_shuffling": disable_shuffling,
**TRAIN_ARGS,
}
)
tokenizer_module = load_tokenizer(model_args)
tokenizer = tokenizer_module["tokenizer"]
template = get_template_and_fix_tokenizer(tokenizer, data_args)
dataset_module = get_dataset(template, model_args, data_args, training_args, stage="sft", **tokenizer_module)
model = load_model(tokenizer, model_args, finetuning_args, training_args.do_train)
data_collator = DataCollatorWithVerbose(tokenizer=tokenizer)
trainer = CustomSeq2SeqTrainer(
model=model,
args=training_args,
finetuning_args=finetuning_args,
data_collator=data_collator,
**dataset_module,
**tokenizer_module,
)
trainer.train()
if disable_shuffling:
assert data_collator.verbose_list[0]["input_ids"] == dataset_module["train_dataset"][0]["input_ids"]
else:
assert data_collator.verbose_list[0]["input_ids"] != dataset_module["train_dataset"][0]["input_ids"]
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/model/test_pissa.py | tests/model/test_pissa.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
from llamafactory.train.test_utils import compare_model, load_infer_model, load_reference_model, load_train_model
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TINY_LLAMA_PISSA = os.getenv("TINY_LLAMA_ADAPTER", "llamafactory/tiny-random-Llama-3-pissa")
TRAIN_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"stage": "sft",
"do_train": True,
"finetuning_type": "lora",
"pissa_init": True,
"pissa_iter": -1,
"dataset": "llamafactory/tiny-supervised-dataset",
"dataset_dir": "ONLINE",
"template": "llama3",
"cutoff_len": 1024,
"output_dir": "dummy_dir",
"overwrite_output_dir": True,
"fp16": True,
}
INFER_ARGS = {
"model_name_or_path": TINY_LLAMA_PISSA,
"adapter_name_or_path": TINY_LLAMA_PISSA,
"adapter_folder": "pissa_init",
"finetuning_type": "lora",
"template": "llama3",
"infer_dtype": "float16",
}
@pytest.mark.xfail(reason="PiSSA initialization is not stable in different platform.")
def test_pissa_train():
model = load_train_model(**TRAIN_ARGS)
ref_model = load_reference_model(TINY_LLAMA_PISSA, TINY_LLAMA_PISSA, use_pissa=True, is_trainable=True)
compare_model(model, ref_model)
@pytest.mark.xfail(reason="Known connection error.")
def test_pissa_inference():
model = load_infer_model(**INFER_ARGS)
ref_model = load_reference_model(TINY_LLAMA_PISSA, TINY_LLAMA_PISSA, use_pissa=True, is_trainable=False)
ref_model = ref_model.merge_and_unload()
compare_model(model, ref_model)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/model/test_base.py | tests/model/test_base.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
from llamafactory.train.test_utils import compare_model, load_infer_model, load_reference_model
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TINY_LLAMA_VALUEHEAD = os.getenv("TINY_LLAMA_VALUEHEAD", "llamafactory/tiny-random-Llama-3-valuehead")
INFER_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"template": "llama3",
"infer_dtype": "float16",
}
def test_base():
model = load_infer_model(**INFER_ARGS)
ref_model = load_reference_model(TINY_LLAMA3)
compare_model(model, ref_model)
@pytest.mark.usefixtures("fix_valuehead_cpu_loading")
def test_valuehead():
model = load_infer_model(add_valuehead=True, **INFER_ARGS)
ref_model = load_reference_model(TINY_LLAMA_VALUEHEAD, add_valuehead=True)
compare_model(model, ref_model)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/model/test_full.py | tests/model/test_full.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
from llamafactory.train.test_utils import load_infer_model, load_train_model
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TRAIN_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"stage": "sft",
"do_train": True,
"finetuning_type": "full",
"dataset": "llamafactory/tiny-supervised-dataset",
"dataset_dir": "ONLINE",
"template": "llama3",
"cutoff_len": 1024,
"output_dir": "dummy_dir",
"overwrite_output_dir": True,
"fp16": True,
}
INFER_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"finetuning_type": "full",
"template": "llama3",
"infer_dtype": "float16",
}
def test_full_train():
model = load_train_model(**TRAIN_ARGS)
for param in model.parameters():
assert param.requires_grad is True
assert param.dtype == torch.float32
def test_full_inference():
model = load_infer_model(**INFER_ARGS)
for param in model.parameters():
assert param.requires_grad is False
assert param.dtype == torch.float16
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/model/test_freeze.py | tests/model/test_freeze.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
from llamafactory.train.test_utils import load_infer_model, load_train_model
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TRAIN_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"stage": "sft",
"do_train": True,
"finetuning_type": "freeze",
"dataset": "llamafactory/tiny-supervised-dataset",
"dataset_dir": "ONLINE",
"template": "llama3",
"cutoff_len": 1024,
"output_dir": "dummy_dir",
"overwrite_output_dir": True,
"fp16": True,
}
INFER_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"finetuning_type": "freeze",
"template": "llama3",
"infer_dtype": "float16",
}
def test_freeze_train_all_modules():
model = load_train_model(freeze_trainable_layers=1, **TRAIN_ARGS)
for name, param in model.named_parameters():
if name.startswith("model.layers.1."):
assert param.requires_grad is True
assert param.dtype == torch.float32
else:
assert param.requires_grad is False
assert param.dtype == torch.float16
def test_freeze_train_extra_modules():
model = load_train_model(freeze_trainable_layers=1, freeze_extra_modules="embed_tokens,lm_head", **TRAIN_ARGS)
for name, param in model.named_parameters():
if name.startswith("model.layers.1.") or any(module in name for module in ["embed_tokens", "lm_head"]):
assert param.requires_grad is True
assert param.dtype == torch.float32
else:
assert param.requires_grad is False
assert param.dtype == torch.float16
def test_freeze_inference():
model = load_infer_model(**INFER_ARGS)
for param in model.parameters():
assert param.requires_grad is False
assert param.dtype == torch.float16
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/model/test_lora.py | tests/model/test_lora.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
import torch
from llamafactory.train.test_utils import (
check_lora_model,
compare_model,
load_infer_model,
load_reference_model,
load_train_model,
)
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TINY_LLAMA_ADAPTER = os.getenv("TINY_LLAMA_ADAPTER", "llamafactory/tiny-random-Llama-3-lora")
TINY_LLAMA_VALUEHEAD = os.getenv("TINY_LLAMA_VALUEHEAD", "llamafactory/tiny-random-Llama-3-valuehead")
TRAIN_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"stage": "sft",
"do_train": True,
"finetuning_type": "lora",
"dataset": "llamafactory/tiny-supervised-dataset",
"dataset_dir": "ONLINE",
"template": "llama3",
"cutoff_len": 1024,
"output_dir": "dummy_dir",
"overwrite_output_dir": True,
"fp16": True,
}
INFER_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"adapter_name_or_path": TINY_LLAMA_ADAPTER,
"finetuning_type": "lora",
"template": "llama3",
"infer_dtype": "float16",
}
def test_lora_train_qv_modules():
model = load_train_model(lora_target="q_proj,v_proj", **TRAIN_ARGS)
linear_modules, _ = check_lora_model(model)
assert linear_modules == {"q_proj", "v_proj"}
def test_lora_train_all_modules():
model = load_train_model(lora_target="all", **TRAIN_ARGS)
linear_modules, _ = check_lora_model(model)
assert linear_modules == {"q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "gate_proj", "down_proj"}
def test_lora_train_extra_modules():
model = load_train_model(additional_target="embed_tokens,lm_head", **TRAIN_ARGS)
_, extra_modules = check_lora_model(model)
assert extra_modules == {"embed_tokens", "lm_head"}
def test_lora_train_old_adapters():
model = load_train_model(adapter_name_or_path=TINY_LLAMA_ADAPTER, create_new_adapter=False, **TRAIN_ARGS)
ref_model = load_reference_model(TINY_LLAMA3, TINY_LLAMA_ADAPTER, use_lora=True, is_trainable=True)
compare_model(model, ref_model)
def test_lora_train_new_adapters():
model = load_train_model(adapter_name_or_path=TINY_LLAMA_ADAPTER, create_new_adapter=True, **TRAIN_ARGS)
ref_model = load_reference_model(TINY_LLAMA3, TINY_LLAMA_ADAPTER, use_lora=True, is_trainable=True)
compare_model(
model, ref_model, diff_keys=["q_proj", "k_proj", "v_proj", "o_proj", "up_proj", "gate_proj", "down_proj"]
)
@pytest.mark.usefixtures("fix_valuehead_cpu_loading")
def test_lora_train_valuehead():
model = load_train_model(add_valuehead=True, **TRAIN_ARGS)
ref_model = load_reference_model(TINY_LLAMA_VALUEHEAD, is_trainable=True, add_valuehead=True)
state_dict = model.state_dict()
ref_state_dict = ref_model.state_dict()
assert torch.allclose(state_dict["v_head.summary.weight"], ref_state_dict["v_head.summary.weight"])
assert torch.allclose(state_dict["v_head.summary.bias"], ref_state_dict["v_head.summary.bias"])
def test_lora_inference():
model = load_infer_model(**INFER_ARGS)
ref_model = load_reference_model(TINY_LLAMA3, TINY_LLAMA_ADAPTER, use_lora=True).merge_and_unload()
compare_model(model, ref_model)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/model/model_utils/test_checkpointing.py | tests/model/model_utils/test_checkpointing.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
import torch
from llamafactory.extras.misc import get_current_device
from llamafactory.train.test_utils import load_train_model
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TRAIN_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"stage": "sft",
"do_train": True,
"finetuning_type": "lora",
"lora_target": "all",
"dataset": "llamafactory/tiny-supervised-dataset",
"dataset_dir": "ONLINE",
"template": "llama3",
"cutoff_len": 1024,
"output_dir": "dummy_dir",
"overwrite_output_dir": True,
"fp16": True,
}
@pytest.mark.parametrize("disable_gradient_checkpointing", [False, True])
def test_vanilla_checkpointing(disable_gradient_checkpointing: bool):
model = load_train_model(disable_gradient_checkpointing=disable_gradient_checkpointing, **TRAIN_ARGS)
for module in filter(lambda m: hasattr(m, "gradient_checkpointing"), model.modules()):
assert getattr(module, "gradient_checkpointing") != disable_gradient_checkpointing
def test_unsloth_gradient_checkpointing():
model = load_train_model(use_unsloth_gc=True, **TRAIN_ARGS)
for module in filter(lambda m: hasattr(m, "gradient_checkpointing"), model.modules()):
assert module._gradient_checkpointing_func.__self__.__name__ == "UnslothGradientCheckpointing"
def test_upcast_layernorm():
model = load_train_model(upcast_layernorm=True, **TRAIN_ARGS)
for name, param in model.named_parameters():
if param.ndim == 1 and "norm" in name:
assert param.dtype == torch.float32
def test_upcast_lmhead_output():
model = load_train_model(upcast_lmhead_output=True, **TRAIN_ARGS)
inputs = torch.randn((1, 16), dtype=torch.float16, device=get_current_device())
outputs: torch.Tensor = model.get_output_embeddings()(inputs)
assert outputs.dtype == torch.float32
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/model/model_utils/test_visual.py | tests/model/model_utils/test_visual.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
import torch
from transformers import AutoConfig, AutoModelForVision2Seq
from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.hparams import FinetuningArguments, ModelArguments
from llamafactory.model.adapter import init_adapter
@pytest.mark.parametrize("freeze_vision_tower", (False, True))
@pytest.mark.parametrize("freeze_multi_modal_projector", (False, True))
@pytest.mark.parametrize("freeze_language_model", (False, True))
def test_visual_full(freeze_vision_tower: bool, freeze_multi_modal_projector: bool, freeze_language_model: bool):
model_args = ModelArguments(model_name_or_path="Qwen/Qwen2-VL-2B-Instruct")
finetuning_args = FinetuningArguments(
finetuning_type="full",
freeze_vision_tower=freeze_vision_tower,
freeze_multi_modal_projector=freeze_multi_modal_projector,
freeze_language_model=freeze_language_model,
)
config = AutoConfig.from_pretrained(model_args.model_name_or_path)
with torch.device("meta"):
model = AutoModelForVision2Seq.from_config(config)
model = init_adapter(config, model, model_args, finetuning_args, is_trainable=True)
for name, param in model.named_parameters():
if any(key in name for key in ["visual.patch_embed", "visual.blocks"]):
assert param.requires_grad != freeze_vision_tower
elif "visual.merger" in name:
assert param.requires_grad != freeze_multi_modal_projector
else:
assert param.requires_grad != freeze_language_model
@pytest.mark.parametrize("freeze_vision_tower,freeze_language_model", ((False, False), (False, True), (True, False)))
def test_visual_lora(freeze_vision_tower: bool, freeze_language_model: bool):
model_args = ModelArguments(model_name_or_path="Qwen/Qwen2-VL-2B-Instruct")
finetuning_args = FinetuningArguments(
finetuning_type="lora", freeze_vision_tower=freeze_vision_tower, freeze_language_model=freeze_language_model
)
config = AutoConfig.from_pretrained(model_args.model_name_or_path)
with torch.device("meta"):
model = AutoModelForVision2Seq.from_config(config)
model = init_adapter(config, model, model_args, finetuning_args, is_trainable=True)
trainable_params, frozen_params = set(), set()
for name, param in model.named_parameters():
if param.requires_grad:
trainable_params.add(name)
else:
frozen_params.add(name)
if is_transformers_version_greater_than("4.52.0"):
visual_param_name = "base_model.model.model.visual.blocks.0.attn.qkv.lora_A.default.weight"
language_param_name = "base_model.model.model.language_model.layers.0.self_attn.q_proj.lora_A.default.weight"
merger_param_name = "base_model.model.model.visual.merger.lora_A.default.weight"
else:
visual_param_name = "base_model.model.visual.blocks.0.attn.qkv.lora_A.default.weight"
language_param_name = "base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight"
merger_param_name = "base_model.model.visual.merger.lora_A.default.weight"
assert (visual_param_name in trainable_params) != freeze_vision_tower
assert (language_param_name in trainable_params) != freeze_language_model
assert (merger_param_name in trainable_params) is False
def test_visual_model_save_load():
# check VLM's state dict: https://github.com/huggingface/transformers/pull/38385
model_args = ModelArguments(model_name_or_path="Qwen/Qwen2-VL-2B-Instruct")
finetuning_args = FinetuningArguments(finetuning_type="full")
config = AutoConfig.from_pretrained(model_args.model_name_or_path)
with torch.device("meta"):
model = AutoModelForVision2Seq.from_config(config)
model = init_adapter(config, model, model_args, finetuning_args, is_trainable=False)
loaded_model_weight = dict(model.named_parameters())
model.save_pretrained(os.path.join("output", "qwen2_vl"), max_shard_size="10GB", safe_serialization=False)
saved_model_weight = torch.load(os.path.join("output", "qwen2_vl", "pytorch_model.bin"), weights_only=False)
if is_transformers_version_greater_than("4.52.0"):
assert "model.language_model.layers.0.self_attn.q_proj.weight" in loaded_model_weight
else:
assert "model.layers.0.self_attn.q_proj.weight" in loaded_model_weight
assert "model.layers.0.self_attn.q_proj.weight" in saved_model_weight
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/model/model_utils/test_add_tokens.py | tests/model/model_utils/test_add_tokens.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
from llamafactory.hparams import ModelArguments
from llamafactory.model import load_tokenizer
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
UNUSED_TOKEN = "<|UNUSED_TOKEN|>"
@pytest.mark.parametrize("special_tokens", [False, True])
def test_add_tokens(special_tokens: bool):
if special_tokens:
model_args = ModelArguments(model_name_or_path=TINY_LLAMA3, add_special_tokens=UNUSED_TOKEN)
else:
model_args = ModelArguments(model_name_or_path=TINY_LLAMA3, add_tokens=UNUSED_TOKEN)
tokenizer = load_tokenizer(model_args)["tokenizer"]
encoded_ids = tokenizer.encode(UNUSED_TOKEN, add_special_tokens=False)
assert len(encoded_ids) == 1
decoded_str = tokenizer.decode(encoded_ids, skip_special_tokens=True)
if special_tokens:
assert decoded_str == ""
else:
assert decoded_str == UNUSED_TOKEN
if __name__ == "__main__":
pytest.main([__file__])
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/model/model_utils/test_attention.py | tests/model/model_utils/test_attention.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
from transformers.utils import is_flash_attn_2_available
# Compatible with Transformers v4 and Transformers v5
try:
from transformers.utils import is_torch_sdpa_available
except ImportError:
def is_torch_sdpa_available():
return True
from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.train.test_utils import load_infer_model
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
INFER_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"template": "llama3",
}
@pytest.mark.xfail(is_transformers_version_greater_than("4.48"), reason="Attention refactor.")
def test_attention():
attention_available = ["disabled"]
if is_torch_sdpa_available():
attention_available.append("sdpa")
if is_flash_attn_2_available():
attention_available.append("fa2")
llama_attention_classes = {
"disabled": "LlamaAttention",
"sdpa": "LlamaSdpaAttention",
"fa2": "LlamaFlashAttention2",
}
for requested_attention in attention_available:
model = load_infer_model(flash_attn=requested_attention, **INFER_ARGS)
for module in model.modules():
if "Attention" in module.__class__.__name__:
assert module.__class__.__name__ == llama_attention_classes[requested_attention]
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/model/model_utils/test_packing.py | tests/model/model_utils/test_packing.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytest
import torch
from llamafactory.model.model_utils.packing import get_seqlens_in_batch, get_unpad_data
@pytest.mark.parametrize(
"attention_mask,golden_seq_lens",
[
(
[
[1, 1, 2, 2, 2, 0],
[1, 2, 2, 3, 3, 3],
],
[2, 3, 1, 2, 3],
),
(
[[1]],
[1],
),
],
)
def test_get_seqlens_in_batch(attention_mask, golden_seq_lens):
attention_mask_with_indices = torch.tensor(attention_mask)
seqlens_in_batch = get_seqlens_in_batch(attention_mask_with_indices)
assert torch.all(seqlens_in_batch == torch.tensor(golden_seq_lens))
@pytest.mark.parametrize(
"attention_mask,golden_indices,golden_cu_seqlens,golden_max_seqlen",
[
(
[
[1, 1, 2, 2, 2, 0],
[1, 2, 2, 3, 3, 3],
],
[0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11],
[0, 2, 5, 6, 8, 11],
3,
),
(
[[1]],
[0],
[0, 1],
1,
),
],
)
def test_get_unpad_data(attention_mask, golden_indices, golden_cu_seqlens, golden_max_seqlen):
attention_mask_with_indices = torch.tensor(attention_mask)
indices, cu_seqlens, max_seqlen_in_batch = get_unpad_data(attention_mask_with_indices)
assert torch.all(indices == torch.tensor(golden_indices))
assert torch.all(cu_seqlens == torch.tensor(golden_cu_seqlens, dtype=torch.int32))
assert max_seqlen_in_batch == golden_max_seqlen
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/model/model_utils/test_misc.py | tests/model/model_utils/test_misc.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
import torch
from transformers import AutoConfig, AutoModelForCausalLM
from llamafactory.model.model_utils.misc import find_expanded_modules
HF_TOKEN = os.getenv("HF_TOKEN")
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
def test_expanded_modules():
config = AutoConfig.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
with torch.device("meta"):
model = AutoModelForCausalLM.from_config(config)
expanded_modules = find_expanded_modules(model, ["q_proj", "v_proj"], num_layer_trainable=4)
assert expanded_modules == [
"model.layers.7.self_attn.q_proj",
"model.layers.7.self_attn.v_proj",
"model.layers.15.self_attn.q_proj",
"model.layers.15.self_attn.v_proj",
"model.layers.23.self_attn.q_proj",
"model.layers.23.self_attn.v_proj",
"model.layers.31.self_attn.q_proj",
"model.layers.31.self_attn.v_proj",
]
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/e2e/test_sglang.py | tests/e2e/test_sglang.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import pytest
from llamafactory.chat import ChatModel
from llamafactory.extras.packages import is_sglang_available
MODEL_NAME = "Qwen/Qwen2.5-0.5B"
INFER_ARGS = {
"model_name_or_path": MODEL_NAME,
"finetuning_type": "lora",
"template": "llama3",
"infer_dtype": "float16",
"infer_backend": "sglang",
"do_sample": False,
"max_new_tokens": 1,
}
MESSAGES = [
{"role": "user", "content": "Hi"},
]
@pytest.mark.runs_on(["cuda"])
@pytest.mark.skipif(not is_sglang_available(), reason="SGLang is not installed")
def test_chat():
r"""Test the SGLang engine's basic chat functionality."""
chat_model = ChatModel(INFER_ARGS)
response = chat_model.chat(MESSAGES)[0]
# TODO: Change to EXPECTED_RESPONSE
print(response.response_text)
@pytest.mark.runs_on(["cuda"])
@pytest.mark.skipif(not is_sglang_available(), reason="SGLang is not installed")
def test_stream_chat():
r"""Test the SGLang engine's streaming chat functionality."""
chat_model = ChatModel(INFER_ARGS)
response = ""
for token in chat_model.stream_chat(MESSAGES):
response += token
print("Complete response:", response)
assert response, "Should receive a non-empty response"
# Run tests if executed directly
if __name__ == "__main__":
if not is_sglang_available():
print("SGLang is not available. Please install it.")
sys.exit(1)
test_chat()
test_stream_chat()
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/e2e/test_chat.py | tests/e2e/test_chat.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
from llamafactory.chat import ChatModel
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
INFER_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"finetuning_type": "lora",
"template": "llama3",
"infer_dtype": "float16",
"do_sample": False,
"max_new_tokens": 1,
}
MESSAGES = [
{"role": "user", "content": "Hi"},
]
EXPECTED_RESPONSE = "_rho"
@pytest.mark.runs_on(["cpu", "mps"])
def test_chat():
chat_model = ChatModel(INFER_ARGS)
assert chat_model.chat(MESSAGES)[0].response_text == EXPECTED_RESPONSE
@pytest.mark.runs_on(["cpu", "mps"])
def test_stream_chat():
chat_model = ChatModel(INFER_ARGS)
response = ""
for token in chat_model.stream_chat(MESSAGES):
response += token
assert response == EXPECTED_RESPONSE
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/e2e/test_train.py | tests/e2e/test_train.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
from llamafactory.train.tuner import export_model, run_exp
DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TINY_LLAMA_ADAPTER = os.getenv("TINY_LLAMA_ADAPTER", "llamafactory/tiny-random-Llama-3-lora")
TRAIN_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"do_train": True,
"finetuning_type": "lora",
"dataset_dir": "REMOTE:" + DEMO_DATA,
"template": "llama3",
"cutoff_len": 1,
"overwrite_output_dir": True,
"per_device_train_batch_size": 1,
"max_steps": 1,
"report_to": "none",
}
INFER_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"adapter_name_or_path": TINY_LLAMA_ADAPTER,
"finetuning_type": "lora",
"template": "llama3",
"infer_dtype": "float16",
}
OS_NAME = os.getenv("OS_NAME", "")
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize(
"stage,dataset",
[
("pt", "c4_demo"),
("sft", "alpaca_en_demo"),
("dpo", "dpo_en_demo"),
("kto", "kto_en_demo"),
pytest.param("rm", "dpo_en_demo", marks=pytest.mark.xfail(OS_NAME.startswith("windows"), reason="OS error.")),
],
)
def test_run_exp(stage: str, dataset: str):
output_dir = os.path.join("output", f"train_{stage}")
run_exp({"stage": stage, "dataset": dataset, "output_dir": output_dir, **TRAIN_ARGS})
assert os.path.exists(output_dir)
@pytest.mark.runs_on(["cpu", "mps"])
def test_export():
export_dir = os.path.join("output", "llama3_export")
export_model({"export_dir": export_dir, **INFER_ARGS})
assert os.path.exists(export_dir)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/eval/test_eval_template.py | tests/eval/test_eval_template.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytest
from llamafactory.eval.template import get_eval_template
@pytest.mark.runs_on(["cpu", "mps"])
def test_eval_template_en():
support_set = [
{
"question": "Fewshot question",
"A": "Fewshot1",
"B": "Fewshot2",
"C": "Fewshot3",
"D": "Fewshot4",
"answer": "B",
}
]
example = {
"question": "Target question",
"A": "Target1",
"B": "Target2",
"C": "Target3",
"D": "Target4",
"answer": "C",
}
template = get_eval_template(name="en")
messages = template.format_example(example, support_set=support_set, subject_name="SubName")
assert messages == [
{
"role": "user",
"content": (
"The following are multiple choice questions (with answers) about SubName.\n\n"
"Fewshot question\nA. Fewshot1\nB. Fewshot2\nC. Fewshot3\nD. Fewshot4\nAnswer:"
),
},
{"role": "assistant", "content": "B"},
{
"role": "user",
"content": "Target question\nA. Target1\nB. Target2\nC. Target3\nD. Target4\nAnswer:",
},
{"role": "assistant", "content": "C"},
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_eval_template_zh():
support_set = [
{
"question": "示例问题",
"A": "示例答案1",
"B": "示例答案2",
"C": "示例答案3",
"D": "示例答案4",
"answer": "B",
}
]
example = {
"question": "目标问题",
"A": "目标答案1",
"B": "目标答案2",
"C": "目标答案3",
"D": "目标答案4",
"answer": "C",
}
template = get_eval_template(name="zh")
messages = template.format_example(example, support_set=support_set, subject_name="主题")
assert messages == [
{
"role": "user",
"content": (
"以下是中国关于主题考试的单项选择题，请选出其中的正确答案。\n\n"
"示例问题\nA. 示例答案1\nB. 示例答案2\nC. 示例答案3\nD. 示例答案4\n答案："
),
},
{"role": "assistant", "content": "B"},
{
"role": "user",
"content": "目标问题\nA. 目标答案1\nB. 目标答案2\nC. 目标答案3\nD. 目标答案4\n答案：",
},
{"role": "assistant", "content": "C"},
]
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/data/test_loader.py | tests/data/test_loader.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
from llamafactory.train.test_utils import load_dataset_module
DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TINY_DATA = os.getenv("TINY_DATA", "llamafactory/tiny-supervised-dataset")
TRAIN_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"stage": "sft",
"do_train": True,
"finetuning_type": "full",
"template": "llama3",
"dataset": TINY_DATA,
"dataset_dir": "ONLINE",
"cutoff_len": 8192,
"output_dir": "dummy_dir",
"overwrite_output_dir": True,
"fp16": True,
}
@pytest.mark.runs_on(["cpu", "mps"])
def test_load_train_only():
dataset_module = load_dataset_module(**TRAIN_ARGS)
assert dataset_module.get("train_dataset") is not None
assert dataset_module.get("eval_dataset") is None
@pytest.mark.runs_on(["cpu", "mps"])
def test_load_val_size():
dataset_module = load_dataset_module(val_size=0.1, **TRAIN_ARGS)
assert dataset_module.get("train_dataset") is not None
assert dataset_module.get("eval_dataset") is not None
@pytest.mark.runs_on(["cpu", "mps"])
def test_load_eval_data():
dataset_module = load_dataset_module(eval_dataset=TINY_DATA, **TRAIN_ARGS)
assert dataset_module.get("train_dataset") is not None
assert dataset_module.get("eval_dataset") is not None
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/data/test_template.py | tests/data/test_template.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import TYPE_CHECKING
import pytest
from transformers import AutoTokenizer
from llamafactory.data import get_template_and_fix_tokenizer
from llamafactory.data.template import parse_template
from llamafactory.hparams import DataArguments
if TYPE_CHECKING:
from transformers import PreTrainedTokenizer
HF_TOKEN = os.getenv("HF_TOKEN")
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TINY_LLAMA4 = os.getenv("TINY_LLAMA4", "llamafactory/tiny-random-Llama-4")
MESSAGES = [
{"role": "user", "content": "How are you"},
{"role": "assistant", "content": "I am fine!"},
{"role": "user", "content": "你好"},
{"role": "assistant", "content": "很高兴认识你！"},
]
MESSAGES_WITH_THOUGHT = [
{"role": "user", "content": "How are you"},
{"role": "assistant", "content": "<think>\nModel thought here\n</think>\n\nI am fine!"},
{"role": "user", "content": "你好"},
{"role": "assistant", "content": "<think>\n模型思考内容\n</think>\n\n很高兴认识你！"},
]
def _check_tokenization(
tokenizer: "PreTrainedTokenizer", batch_input_ids: list[list[int]], batch_text: list[str]
) -> None:
r"""Check token ids and texts.
encode(text) == token_ids
decode(token_ids) == text
"""
for input_ids, text in zip(batch_input_ids, batch_text):
assert tokenizer.encode(text, add_special_tokens=False) == input_ids
assert tokenizer.decode(input_ids) == text
def _check_template(
model_id: str,
template_name: str,
prompt_str: str,
answer_str: str,
use_fast: bool,
messages: list[dict[str, str]] = MESSAGES,
) -> None:
r"""Check template.
Args:
model_id: the model id on hugging face hub.
template_name: the template name.
prompt_str: the string corresponding to the prompt part.
answer_str: the string corresponding to the answer part.
use_fast: whether to use fast tokenizer.
messages: the list of messages.
"""
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=use_fast, token=HF_TOKEN)
content_str = tokenizer.apply_chat_template(messages, tokenize=False)
content_ids = tokenizer.apply_chat_template(messages, tokenize=True)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template=template_name))
prompt_ids, answer_ids = template.encode_oneturn(tokenizer, messages)
assert content_str == prompt_str + answer_str
assert content_ids == prompt_ids + answer_ids
_check_tokenization(tokenizer, (prompt_ids, answer_ids), (prompt_str, answer_str))
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("use_fast", [True, False])
def test_encode_oneturn(use_fast: bool):
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3, use_fast=use_fast)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES)
prompt_str = (
"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\nI am fine!<|eot_id|>"
"<|start_header_id|>user<|end_header_id|>\n\n你好<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
answer_str = "很高兴认识你！<|eot_id|>"
_check_tokenization(tokenizer, (prompt_ids, answer_ids), (prompt_str, answer_str))
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("use_fast", [True, False])
def test_encode_multiturn(use_fast: bool):
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3, use_fast=use_fast)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
encoded_pairs = template.encode_multiturn(tokenizer, MESSAGES)
prompt_str_1 = (
"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\nHow are you<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
answer_str_1 = "I am fine!<|eot_id|>"
prompt_str_2 = (
"<|start_header_id|>user<|end_header_id|>\n\n你好<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
)
answer_str_2 = "很高兴认识你！<|eot_id|>"
_check_tokenization(
tokenizer,
(encoded_pairs[0][0], encoded_pairs[0][1], encoded_pairs[1][0], encoded_pairs[1][1]),
(prompt_str_1, answer_str_1, prompt_str_2, answer_str_2),
)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("use_fast", [True, False])
@pytest.mark.parametrize("cot_messages", [True, False])
@pytest.mark.parametrize("enable_thinking", [True, False, None])
def test_reasoning_encode_oneturn(use_fast: bool, cot_messages: bool, enable_thinking: bool):
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", use_fast=use_fast)
data_args = DataArguments(template="qwen3", enable_thinking=enable_thinking)
template = get_template_and_fix_tokenizer(tokenizer, data_args)
prompt_ids, answer_ids = template.encode_oneturn(tokenizer, MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES)
prompt_str = (
f"<|im_start|>user\n{MESSAGES[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
f"{MESSAGES[1]['content']}<|im_end|>\n"
f"<|im_start|>user\n{MESSAGES[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
)
if not cot_messages or enable_thinking is False:
answer_str = f"{MESSAGES[3]['content']}<|im_end|>\n"
if enable_thinking:
answer_str = "<think>\n\n</think>\n\n" + answer_str
else:
prompt_str = prompt_str + "<think>\n\n</think>\n\n"
else:
answer_str = f"{MESSAGES_WITH_THOUGHT[3]['content']}<|im_end|>\n"
_check_tokenization(tokenizer, (prompt_ids, answer_ids), (prompt_str, answer_str))
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("use_fast", [True, False])
@pytest.mark.parametrize("cot_messages", [True, False])
@pytest.mark.parametrize("enable_thinking", [True, False, None])
def test_reasoning_encode_multiturn(use_fast: bool, cot_messages: bool, enable_thinking: bool):
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", use_fast=use_fast)
data_args = DataArguments(template="qwen3", enable_thinking=enable_thinking)
template = get_template_and_fix_tokenizer(tokenizer, data_args)
encoded_pairs = template.encode_multiturn(tokenizer, MESSAGES_WITH_THOUGHT if cot_messages else MESSAGES)
messages = MESSAGES if not cot_messages or enable_thinking is False else MESSAGES_WITH_THOUGHT
prompt_str_1 = f"<|im_start|>user\n{MESSAGES[0]['content']}<|im_end|>\n<|im_start|>assistant\n"
answer_str_1 = f"{messages[1]['content']}<|im_end|>\n"
prompt_str_2 = f"<|im_start|>user\n{MESSAGES[2]['content']}<|im_end|>\n<|im_start|>assistant\n"
answer_str_2 = f"{messages[3]['content']}<|im_end|>\n"
if not cot_messages or enable_thinking is False:
if enable_thinking:
answer_str_1 = "<think>\n\n</think>\n\n" + answer_str_1
answer_str_2 = "<think>\n\n</think>\n\n" + answer_str_2
else:
prompt_str_1 = prompt_str_1 + "<think>\n\n</think>\n\n"
prompt_str_2 = prompt_str_2 + "<think>\n\n</think>\n\n"
_check_tokenization(
tokenizer,
(encoded_pairs[0][0], encoded_pairs[0][1], encoded_pairs[1][0], encoded_pairs[1][1]),
(prompt_str_1, answer_str_1, prompt_str_2, answer_str_2),
)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("use_fast", [True, False])
def test_jinja_template(use_fast: bool):
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3, use_fast=use_fast)
ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3, use_fast=use_fast)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
tokenizer.chat_template = template._get_jinja_template(tokenizer) # llama3 template no replace
assert tokenizer.chat_template != ref_tokenizer.chat_template
assert tokenizer.apply_chat_template(MESSAGES) == ref_tokenizer.apply_chat_template(MESSAGES)
@pytest.mark.runs_on(["cpu", "mps"])
def test_ollama_modelfile():
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
assert template.get_ollama_modelfile(tokenizer) == (
"# ollama modelfile auto-generated by llamafactory\n\n"
"FROM .\n\n"
'TEMPLATE """<|begin_of_text|>'
"{{ if .System }}<|start_header_id|>system<|end_header_id|>\n\n{{ .System }}<|eot_id|>{{ end }}"
'{{ range .Messages }}{{ if eq .Role "user" }}<|start_header_id|>user<|end_header_id|>\n\n{{ .Content }}'
"<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
'{{ else if eq .Role "assistant" }}{{ .Content }}<|eot_id|>{{ end }}{{ end }}"""\n\n'
'PARAMETER stop "<|eom_id|>"\n'
'PARAMETER stop "<|eot_id|>"\n'
"PARAMETER num_ctx 4096\n"
)
@pytest.mark.runs_on(["cpu", "mps"])
def test_get_stop_token_ids():
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3)
template = get_template_and_fix_tokenizer(tokenizer, DataArguments(template="llama3"))
assert set(template.get_stop_token_ids(tokenizer)) == {128008, 128009}
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
@pytest.mark.parametrize("use_fast", [True, False])
def test_gemma_template(use_fast: bool):
prompt_str = (
f"<bos><start_of_turn>user\n{MESSAGES[0]['content']}<end_of_turn>\n"
f"<start_of_turn>model\n{MESSAGES[1]['content']}<end_of_turn>\n"
f"<start_of_turn>user\n{MESSAGES[2]['content']}<end_of_turn>\n"
"<start_of_turn>model\n"
)
answer_str = f"{MESSAGES[3]['content']}<end_of_turn>\n"
_check_template("google/gemma-3-4b-it", "gemma", prompt_str, answer_str, use_fast)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
@pytest.mark.parametrize("use_fast", [True, False])
def test_gemma2_template(use_fast: bool):
prompt_str = (
f"<bos><start_of_turn>user\n{MESSAGES[0]['content']}<end_of_turn>\n"
f"<start_of_turn>model\n{MESSAGES[1]['content']}<end_of_turn>\n"
f"<start_of_turn>user\n{MESSAGES[2]['content']}<end_of_turn>\n"
"<start_of_turn>model\n"
)
answer_str = f"{MESSAGES[3]['content']}<end_of_turn>\n"
_check_template("google/gemma-2-2b-it", "gemma2", prompt_str, answer_str, use_fast)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
@pytest.mark.parametrize("use_fast", [True, False])
def test_llama3_template(use_fast: bool):
prompt_str = (
f"<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{MESSAGES[0]['content']}<|eot_id|>"
f"<|start_header_id|>assistant<|end_header_id|>\n\n{MESSAGES[1]['content']}<|eot_id|>"
f"<|start_header_id|>user<|end_header_id|>\n\n{MESSAGES[2]['content']}<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
)
answer_str = f"{MESSAGES[3]['content']}<|eot_id|>"
_check_template("meta-llama/Meta-Llama-3-8B-Instruct", "llama3", prompt_str, answer_str, use_fast)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize(
"use_fast", [True, pytest.param(False, marks=pytest.mark.xfail(reason="Llama 4 has no slow tokenizer."))]
)
def test_llama4_template(use_fast: bool):
prompt_str = (
f"<|begin_of_text|><|header_start|>user<|header_end|>\n\n{MESSAGES[0]['content']}<|eot|>"
f"<|header_start|>assistant<|header_end|>\n\n{MESSAGES[1]['content']}<|eot|>"
f"<|header_start|>user<|header_end|>\n\n{MESSAGES[2]['content']}<|eot|>"
"<|header_start|>assistant<|header_end|>\n\n"
)
answer_str = f"{MESSAGES[3]['content']}<|eot|>"
_check_template(TINY_LLAMA4, "llama4", prompt_str, answer_str, use_fast)
@pytest.mark.parametrize(
"use_fast",
[
pytest.param(True, marks=pytest.mark.xfail(not HF_TOKEN, reason="Authorization.")),
pytest.param(False, marks=pytest.mark.xfail(reason="Phi-4 slow tokenizer is broken.")),
],
)
@pytest.mark.runs_on(["cpu", "mps"])
def test_phi4_template(use_fast: bool):
prompt_str = (
f"<|im_start|>user<|im_sep|>{MESSAGES[0]['content']}<|im_end|>"
f"<|im_start|>assistant<|im_sep|>{MESSAGES[1]['content']}<|im_end|>"
f"<|im_start|>user<|im_sep|>{MESSAGES[2]['content']}<|im_end|>"
"<|im_start|>assistant<|im_sep|>"
)
answer_str = f"{MESSAGES[3]['content']}<|im_end|>"
_check_template("microsoft/phi-4", "phi4", prompt_str, answer_str, use_fast)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.xfail(not HF_TOKEN, reason="Authorization.")
@pytest.mark.parametrize("use_fast", [True, False])
def test_qwen2_5_template(use_fast: bool):
prompt_str = (
"<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n"
f"<|im_start|>user\n{MESSAGES[0]['content']}<|im_end|>\n"
f"<|im_start|>assistant\n{MESSAGES[1]['content']}<|im_end|>\n"
f"<|im_start|>user\n{MESSAGES[2]['content']}<|im_end|>\n"
"<|im_start|>assistant\n"
)
answer_str = f"{MESSAGES[3]['content']}<|im_end|>\n"
_check_template("Qwen/Qwen2.5-7B-Instruct", "qwen", prompt_str, answer_str, use_fast)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("use_fast", [True, False])
@pytest.mark.parametrize("cot_messages", [True, False])
def test_qwen3_template(use_fast: bool, cot_messages: bool):
prompt_str = (
f"<|im_start|>user\n{MESSAGES[0]['content']}<|im_end|>\n"
f"<|im_start|>assistant\n{MESSAGES[1]['content']}<|im_end|>\n"
f"<|im_start|>user\n{MESSAGES[2]['content']}<|im_end|>\n"
"<|im_start|>assistant\n"
)
if not cot_messages:
answer_str = f"<think>\n\n</think>\n\n{MESSAGES[3]['content']}<|im_end|>\n"
messages = MESSAGES
else:
answer_str = f"{MESSAGES_WITH_THOUGHT[3]['content']}<|im_end|>\n"
messages = MESSAGES_WITH_THOUGHT
_check_template("Qwen/Qwen3-8B", "qwen3", prompt_str, answer_str, use_fast, messages=messages)
@pytest.mark.runs_on(["cpu", "mps"])
def test_parse_llama3_template():
tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3, token=HF_TOKEN)
template = parse_template(tokenizer)
assert template.format_user.slots == [
"<|start_header_id|>user<|end_header_id|>\n\n{{content}}<|eot_id|>"
"<|start_header_id|>assistant<|end_header_id|>\n\n"
]
assert template.format_assistant.slots == ["{{content}}<|eot_id|>"]
assert template.format_system.slots == ["<|start_header_id|>system<|end_header_id|>\n\n{{content}}<|eot_id|>"]
assert template.format_prefix.slots == ["<|begin_of_text|>"]
assert template.default_system == ""
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.xfail(not HF_TOKEN, reason="Authorization.")
def test_parse_qwen_template():
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct", token=HF_TOKEN)
template = parse_template(tokenizer)
assert template.__class__.__name__ == "Template"
assert template.format_user.slots == ["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]
assert template.format_assistant.slots == ["{{content}}<|im_end|>\n"]
assert template.format_system.slots == ["<|im_start|>system\n{{content}}<|im_end|>\n"]
assert template.format_prefix.slots == []
assert template.default_system == "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.xfail(not HF_TOKEN, reason="Authorization.")
def test_parse_qwen3_template():
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", token=HF_TOKEN)
template = parse_template(tokenizer)
assert template.__class__.__name__ == "ReasoningTemplate"
assert template.format_user.slots == ["<|im_start|>user\n{{content}}<|im_end|>\n<|im_start|>assistant\n"]
assert template.format_assistant.slots == ["{{content}}<|im_end|>\n"]
assert template.format_system.slots == ["<|im_start|>system\n{{content}}<|im_end|>\n"]
assert template.format_prefix.slots == []
assert template.default_system == ""
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/data/test_mm_plugin.py | tests/data/test_mm_plugin.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import TYPE_CHECKING, Any
import numpy as np
import pytest
import torch
from PIL import Image
from llamafactory.data.mm_plugin import get_mm_plugin
from llamafactory.extras.packages import is_transformers_version_greater_than
from llamafactory.hparams import get_infer_args
from llamafactory.model import load_tokenizer
if TYPE_CHECKING:
from transformers import PreTrainedTokenizer, ProcessorMixin
from transformers.image_processing_utils import BaseImageProcessor
from llamafactory.data.mm_plugin import BasePlugin
from llamafactory.model.loader import TokenizerModule
HF_TOKEN = os.getenv("HF_TOKEN")
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TINY_LLAMA4 = os.getenv("TINY_LLAMA4", "llamafactory/tiny-random-Llama-4")
MM_MESSAGES = [
{"role": "user", "content": "<image>What is in this image?"},
{"role": "assistant", "content": "A cat."},
]
OMNI_MESSAGES = [
{"role": "user", "content": "<image>What is in this image?"},
{"role": "assistant", "content": "A cat."},
{"role": "user", "content": "<audio>What is in this audio?"},
{"role": "assistant", "content": "Nothing."},
]
TEXT_MESSAGES = [
{"role": "user", "content": "How are you"},
{"role": "assistant", "content": "I am fine!"},
]
VIDEO_MESSAGES = [
{"role": "user", "content": "<video>What is in this viode?"},
{"role": "assistant", "content": "A cat."},
]
AUDIOS = [np.zeros(1600)]
IMAGES = [Image.new("RGB", (32, 32), (255, 255, 255))]
VIDEOS = [[Image.new("RGB", (32, 32), (255, 255, 255))] * 4]
NO_IMAGES = []
NO_VIDEOS = []
NO_AUDIOS = []
IMGLENS = [1]
AUDLENS = [1]
NO_IMGLENS = [0]
NO_VIDLENS = [0]
NO_AUDLENS = [0]
INPUT_IDS = [0, 1, 2, 3, 4]
LABELS = [0, 1, 2, 3, 4]
BATCH_IDS = [[1] * 1024]
def _get_mm_inputs(processor: "ProcessorMixin") -> dict[str, "torch.Tensor"]:
image_processor: BaseImageProcessor = getattr(processor, "image_processor")
return image_processor(images=IMAGES, return_tensors="pt")
def _get_omni_inputs(processor: "ProcessorMixin") -> dict[str, "torch.Tensor"]:
mm_inputs = {}
image_processor: BaseImageProcessor = getattr(processor, "image_processor", None)
feature_extractor = getattr(processor, "feature_extractor", None)
mm_inputs.update(image_processor(IMAGES, return_tensors="pt"))
mm_inputs.update(
feature_extractor(
AUDIOS,
sampling_rate=getattr(processor, "audio_sampling_rate", 16000),
return_attention_mask=True,
padding="max_length",
return_tensors="pt",
)
)
mm_inputs["feature_attention_mask"] = mm_inputs.pop("attention_mask")
return mm_inputs
def _is_close(batch_a: dict[str, Any], batch_b: dict[str, Any]) -> None:
assert batch_a.keys() == batch_b.keys()
for key in batch_a.keys():
if isinstance(batch_a[key], torch.Tensor):
assert torch.allclose(batch_a[key], batch_b[key], rtol=1e-4, atol=1e-5)
elif isinstance(batch_a[key], list) and all(isinstance(item, torch.Tensor) for item in batch_a[key]):
assert len(batch_a[key]) == len(batch_b[key])
for tensor_a, tensor_b in zip(batch_a[key], batch_b[key]):
assert torch.allclose(tensor_a, tensor_b, rtol=1e-4, atol=1e-5)
else:
assert batch_a[key] == batch_b[key]
def _load_tokenizer_module(model_name_or_path: str) -> "TokenizerModule":
model_args, *_ = get_infer_args({"model_name_or_path": model_name_or_path, "template": "default"})
return load_tokenizer(model_args)
def _check_plugin(
plugin: "BasePlugin",
tokenizer: "PreTrainedTokenizer",
processor: "ProcessorMixin",
expected_mm_messages: list[dict[str, str]] = MM_MESSAGES,
expected_input_ids: list[int] = INPUT_IDS,
expected_labels: list[int] = LABELS,
expected_mm_inputs: dict[str, Any] = {},
expected_no_mm_inputs: dict[str, Any] = {},
) -> None:
if plugin.__class__.__name__ == "Qwen2OmniPlugin": # test omni_messages
assert plugin.process_messages(OMNI_MESSAGES, IMAGES, NO_VIDEOS, AUDIOS, processor) == expected_mm_messages
assert plugin.process_token_ids(INPUT_IDS, LABELS, IMAGES, NO_VIDEOS, AUDIOS, tokenizer, processor) == (
expected_input_ids,
expected_labels,
)
_is_close(
plugin.get_mm_inputs(IMAGES, NO_VIDEOS, AUDIOS, IMGLENS, NO_VIDLENS, AUDLENS, BATCH_IDS, processor),
expected_mm_inputs,
)
elif plugin.__class__.__name__ == "Qwen3VLPlugin": # only check replacement
assert plugin.process_messages(VIDEO_MESSAGES, NO_IMAGES, VIDEOS, NO_AUDIOS, processor) == expected_mm_messages
elif plugin.__class__.__name__ != "BasePlugin": # test mm_messages
assert plugin.process_messages(MM_MESSAGES, IMAGES, NO_VIDEOS, NO_AUDIOS, processor) == expected_mm_messages
assert plugin.process_token_ids(INPUT_IDS, LABELS, IMAGES, NO_VIDEOS, NO_AUDIOS, tokenizer, processor) == (
expected_input_ids,
expected_labels,
)
_is_close(
plugin.get_mm_inputs(IMAGES, NO_VIDEOS, NO_AUDIOS, IMGLENS, NO_VIDLENS, NO_AUDLENS, BATCH_IDS, processor),
expected_mm_inputs,
)
# test text_messages
assert plugin.process_messages(TEXT_MESSAGES, NO_IMAGES, NO_VIDEOS, NO_AUDIOS, processor) == TEXT_MESSAGES
assert plugin.process_token_ids(INPUT_IDS, LABELS, NO_IMAGES, NO_VIDEOS, NO_AUDIOS, tokenizer, processor) == (
INPUT_IDS,
LABELS,
)
_is_close(
plugin.get_mm_inputs(
NO_IMAGES, NO_VIDEOS, NO_AUDIOS, NO_IMGLENS, NO_VIDLENS, NO_AUDLENS, BATCH_IDS, processor
),
expected_no_mm_inputs,
)
@pytest.mark.runs_on(["cpu", "mps"])
def test_base_plugin():
tokenizer_module = _load_tokenizer_module(model_name_or_path=TINY_LLAMA3)
base_plugin = get_mm_plugin(name="base")
check_inputs = {"plugin": base_plugin, **tokenizer_module}
_check_plugin(**check_inputs)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
@pytest.mark.skipif(not is_transformers_version_greater_than("4.50.0"), reason="Requires transformers>=4.50.0")
def test_gemma3_plugin():
image_seqlen = 256
tokenizer_module = _load_tokenizer_module(model_name_or_path="google/gemma-3-4b-it")
gemma3_plugin = get_mm_plugin(name="gemma3", image_token="<image_soft_token>")
image_tokens_expanded = "<image_soft_token>" * image_seqlen
check_inputs = {"plugin": gemma3_plugin, **tokenizer_module}
check_inputs["expected_mm_messages"] = [
{
key: value.replace("<image>", f"\n\n<start_of_image>{image_tokens_expanded}<end_of_image>\n\n")
for key, value in message.items()
}
for message in MM_MESSAGES
]
check_inputs["expected_mm_inputs"] = _get_mm_inputs(tokenizer_module["processor"])
check_inputs["expected_mm_inputs"].pop("num_crops")
check_inputs["expected_mm_inputs"]["token_type_ids"] = [[0] * 1024]
check_inputs["expected_no_mm_inputs"] = {"token_type_ids": [[0] * 1024]}
_check_plugin(**check_inputs)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.skipif(not is_transformers_version_greater_than("4.52.0"), reason="Requires transformers>=4.52.0")
def test_internvl_plugin():
image_seqlen = 256
tokenizer_module = _load_tokenizer_module(model_name_or_path="OpenGVLab/InternVL3-1B-hf")
internvl_plugin = get_mm_plugin("intern_vl", image_token="<image>", video_token="<video>")
check_inputs = {"plugin": internvl_plugin, **tokenizer_module}
check_inputs["expected_mm_messages"] = [
{
key: value.replace("<image>", f"<img>{'<IMG_CONTEXT>' * image_seqlen * 1}</img>")
for key, value in message.items()
}
for message in MM_MESSAGES
]
check_inputs["expected_mm_inputs"] = _get_mm_inputs(tokenizer_module["processor"])
check_inputs["expected_mm_inputs"].pop("num_patches", None)
_check_plugin(**check_inputs)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.skipif(not is_transformers_version_greater_than("4.51.0"), reason="Requires transformers>=4.51.0")
def test_llama4_plugin():
tokenizer_module = _load_tokenizer_module(model_name_or_path=TINY_LLAMA4)
processor = tokenizer_module["processor"]
llama4_plugin = get_mm_plugin(name="llama4", image_token="<|image|>")
check_inputs = {"plugin": llama4_plugin, **tokenizer_module}
mm_inputs = _get_mm_inputs(tokenizer_module["processor"])
image_height, image_width = mm_inputs["pixel_values"][0].shape[-2:]
num_patches_per_chunk = int(
(image_height // processor.patch_size) * (image_width // processor.patch_size) // processor.downsample_ratio
)
aspect_ratios = mm_inputs.pop("aspect_ratios")
tokens_for_this_image = processor._prompt_split_image(aspect_ratios[0], num_patches_per_chunk)
check_inputs["expected_mm_messages"] = [
{key: value.replace("<image>", tokens_for_this_image) for key, value in message.items()}
for message in MM_MESSAGES
]
check_inputs["expected_mm_inputs"] = mm_inputs
_check_plugin(**check_inputs)
@pytest.mark.runs_on(["cpu", "mps"])
def test_llava_plugin():
image_seqlen = 576
tokenizer_module = _load_tokenizer_module(model_name_or_path="llava-hf/llava-1.5-7b-hf")
llava_plugin = get_mm_plugin(name="llava", image_token="<image>")
check_inputs = {"plugin": llava_plugin, **tokenizer_module}
check_inputs["expected_mm_messages"] = [
{key: value.replace("<image>", "<image>" * image_seqlen) for key, value in message.items()}
for message in MM_MESSAGES
]
check_inputs["expected_mm_inputs"] = _get_mm_inputs(tokenizer_module["processor"])
_check_plugin(**check_inputs)
@pytest.mark.runs_on(["cpu", "mps"])
def test_llava_next_plugin():
image_seqlen = 1176
tokenizer_module = _load_tokenizer_module(model_name_or_path="llava-hf/llava-v1.6-vicuna-7b-hf")
llava_next_plugin = get_mm_plugin(name="llava_next", image_token="<image>")
check_inputs = {"plugin": llava_next_plugin, **tokenizer_module}
check_inputs["expected_mm_messages"] = [
{key: value.replace("<image>", "<image>" * image_seqlen) for key, value in message.items()}
for message in MM_MESSAGES
]
check_inputs["expected_mm_inputs"] = _get_mm_inputs(tokenizer_module["processor"])
_check_plugin(**check_inputs)
@pytest.mark.runs_on(["cpu", "mps"])
def test_llava_next_video_plugin():
image_seqlen = 1176
tokenizer_module = _load_tokenizer_module(model_name_or_path="llava-hf/LLaVA-NeXT-Video-7B-hf")
llava_next_video_plugin = get_mm_plugin(name="llava_next_video", image_token="<image>", video_token="<video>")
check_inputs = {"plugin": llava_next_video_plugin, **tokenizer_module}
check_inputs["expected_mm_messages"] = [
{key: value.replace("<image>", "<image>" * image_seqlen) for key, value in message.items()}
for message in MM_MESSAGES
]
check_inputs["expected_mm_inputs"] = _get_mm_inputs(tokenizer_module["processor"])
_check_plugin(**check_inputs)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.skipif(not HF_TOKEN, reason="Gated model.")
def test_paligemma_plugin():
image_seqlen = 256
tokenizer_module = _load_tokenizer_module(model_name_or_path="google/paligemma-3b-pt-224")
paligemma_plugin = get_mm_plugin(name="paligemma", image_token="<image>")
check_inputs = {"plugin": paligemma_plugin, **tokenizer_module}
check_inputs["expected_mm_messages"] = [
{key: value.replace("<image>", "") for key, value in message.items()} for message in MM_MESSAGES
]
check_inputs["expected_input_ids"] = [
tokenizer_module["tokenizer"].convert_tokens_to_ids(paligemma_plugin.image_token)
] * image_seqlen + INPUT_IDS
check_inputs["expected_labels"] = [-100] * image_seqlen + LABELS
check_inputs["expected_mm_inputs"] = _get_mm_inputs(tokenizer_module["processor"])
check_inputs["expected_mm_inputs"]["token_type_ids"] = [[0] * image_seqlen + [1] * (1024 - image_seqlen)]
check_inputs["expected_no_mm_inputs"] = {"token_type_ids": [[1] * 1024]}
_check_plugin(**check_inputs)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.skipif(not is_transformers_version_greater_than("4.50.0"), reason="Requires transformers>=4.50.0")
def test_pixtral_plugin():
image_slice_height, image_slice_width = 2, 2
tokenizer_module = _load_tokenizer_module(model_name_or_path="mistral-community/pixtral-12b")
pixtral_plugin = get_mm_plugin(name="pixtral", image_token="[IMG]")
check_inputs = {"plugin": pixtral_plugin, **tokenizer_module}
check_inputs["expected_mm_messages"] = [
{
key: value.replace(
"<image>",
("{}[IMG_BREAK]".format("[IMG]" * image_slice_width) * image_slice_height).rsplit("[IMG_BREAK]", 1)[0]
+ "[IMG_END]",
)
for key, value in message.items()
}
for message in MM_MESSAGES
]
check_inputs["expected_mm_inputs"] = _get_mm_inputs(tokenizer_module["processor"])
check_inputs["expected_mm_inputs"]["pixel_values"] = check_inputs["expected_mm_inputs"]["pixel_values"][0]
_check_plugin(**check_inputs)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.skipif(not is_transformers_version_greater_than("4.52.0"), reason="Requires transformers>=4.52.0")
def test_qwen2_omni_plugin():
image_seqlen, audio_seqlen = 4, 2
tokenizer_module = _load_tokenizer_module(model_name_or_path="Qwen/Qwen2.5-Omni-7B")
qwen2_omni_plugin = get_mm_plugin(
name="qwen2_omni",
image_token="<|IMAGE|>",
video_token="<|VIDEO|>",
audio_token="<|AUDIO|>",
vision_bos_token="<|vision_bos|>",
vision_eos_token="<|vision_eos|>",
audio_bos_token="<|audio_bos|>",
audio_eos_token="<|audio_eos|>",
)
check_inputs = {"plugin": qwen2_omni_plugin, **tokenizer_module}
check_inputs["expected_mm_messages"] = [
{
key: (
value.replace("<image>", f"<|vision_bos|>{'<|IMAGE|>' * image_seqlen}<|vision_eos|>").replace(
"<audio>", f"<|audio_bos|>{'<|AUDIO|>' * audio_seqlen}<|audio_eos|>"
)
)
for key, value in message.items()
}
for message in OMNI_MESSAGES
]
check_inputs["expected_mm_inputs"] = _get_omni_inputs(tokenizer_module["processor"])
_check_plugin(**check_inputs)
@pytest.mark.runs_on(["cpu", "mps"])
def test_qwen2_vl_plugin():
image_seqlen = 4
tokenizer_module = _load_tokenizer_module(model_name_or_path="Qwen/Qwen2-VL-7B-Instruct")
qwen2_vl_plugin = get_mm_plugin(name="qwen2_vl", image_token="<|image_pad|>")
check_inputs = {"plugin": qwen2_vl_plugin, **tokenizer_module}
check_inputs["expected_mm_messages"] = [
{
key: value.replace("<image>", "<|vision_start|>{}<|vision_end|>".format("<|image_pad|>" * image_seqlen))
for key, value in message.items()
}
for message in MM_MESSAGES
]
check_inputs["expected_mm_inputs"] = _get_mm_inputs(tokenizer_module["processor"])
_check_plugin(**check_inputs)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.skipif(not is_transformers_version_greater_than("4.57.0"), reason="Requires transformers>=4.57.0")
def test_qwen3_vl_plugin():
frame_seqlen = 1
tokenizer_module = _load_tokenizer_module(model_name_or_path="Qwen/Qwen3-VL-30B-A3B-Instruct")
qwen3_vl_plugin = get_mm_plugin(name="qwen3_vl", video_token="<|video_pad|>")
check_inputs = {"plugin": qwen3_vl_plugin, **tokenizer_module}
check_inputs["expected_mm_messages"] = [
{
key: value.replace(
"<video>", # little different with original processor for default `fps=2` in our repo
"<0.2 seconds><|vision_start|>{}<|vision_end|><1.2 seconds><|vision_start|>{}<|vision_end|>".format(
"<|video_pad|>" * frame_seqlen, "<|video_pad|>" * frame_seqlen
),
)
for key, value in message.items()
}
for message in VIDEO_MESSAGES
]
_check_plugin(**check_inputs)
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.skipif(not is_transformers_version_greater_than("4.47.0"), reason="Requires transformers>=4.47.0")
def test_video_llava_plugin():
image_seqlen = 256
tokenizer_module = _load_tokenizer_module(model_name_or_path="LanguageBind/Video-LLaVA-7B-hf")
video_llava_plugin = get_mm_plugin(name="video_llava", image_token="<image>", video_token="<video>")
check_inputs = {"plugin": video_llava_plugin, **tokenizer_module}
check_inputs["expected_mm_messages"] = [
{key: value.replace("<image>", "<image>" * image_seqlen) for key, value in message.items()}
for message in MM_MESSAGES
]
check_inputs["expected_mm_inputs"] = _get_mm_inputs(tokenizer_module["processor"])
_check_plugin(**check_inputs)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/data/test_converter.py | tests/data/test_converter.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytest
from llamafactory.data import Role
from llamafactory.data.converter import get_dataset_converter
from llamafactory.data.parser import DatasetAttr
from llamafactory.hparams import DataArguments
@pytest.mark.runs_on(["cpu", "mps"])
def test_alpaca_converter():
dataset_attr = DatasetAttr("hf_hub", "llamafactory/tiny-supervised-dataset")
data_args = DataArguments()
example = {
"instruction": "Solve the math problem.",
"input": "3 + 4",
"output": "The answer is 7.",
}
dataset_converter = get_dataset_converter("alpaca", dataset_attr, data_args)
assert dataset_converter(example) == {
"_prompt": [{"role": Role.USER.value, "content": "Solve the math problem.\n3 + 4"}],
"_response": [{"role": Role.ASSISTANT.value, "content": "The answer is 7."}],
"_system": "",
"_tools": "",
"_images": None,
"_videos": None,
"_audios": None,
}
@pytest.mark.runs_on(["cpu", "mps"])
def test_sharegpt_converter():
dataset_attr = DatasetAttr("hf_hub", "llamafactory/tiny-supervised-dataset")
data_args = DataArguments()
example = {
"conversations": [
{"from": "system", "value": "You are a helpful assistant."},
{"from": "human", "value": "Solve the math problem.\n3 + 4"},
{"from": "gpt", "value": "The answer is 7."},
]
}
dataset_converter = get_dataset_converter("sharegpt", dataset_attr, data_args)
assert dataset_converter(example) == {
"_prompt": [{"role": Role.USER.value, "content": "Solve the math problem.\n3 + 4"}],
"_response": [{"role": Role.ASSISTANT.value, "content": "The answer is 7."}],
"_system": "You are a helpful assistant.",
"_tools": "",
"_images": None,
"_videos": None,
"_audios": None,
}
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/data/test_collator.py | tests/data/test_collator.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
import torch
from PIL import Image
from transformers import AutoConfig, AutoModelForVision2Seq
from llamafactory.data import get_template_and_fix_tokenizer
from llamafactory.data.collator import MultiModalDataCollatorForSeq2Seq, prepare_4d_attention_mask
from llamafactory.extras.constants import IGNORE_INDEX
from llamafactory.hparams import get_infer_args
from llamafactory.model import load_tokenizer
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
@pytest.mark.runs_on(["cpu", "mps"])
def test_base_collator():
model_args, data_args, *_ = get_infer_args({"model_name_or_path": TINY_LLAMA3, "template": "default"})
tokenizer_module = load_tokenizer(model_args)
template = get_template_and_fix_tokenizer(tokenizer_module["tokenizer"], data_args)
data_collator = MultiModalDataCollatorForSeq2Seq(
template=template,
pad_to_multiple_of=8,
label_pad_token_id=IGNORE_INDEX,
**tokenizer_module,
)
p = tokenizer_module["tokenizer"].pad_token_id
q = IGNORE_INDEX
features = [
{
"input_ids": [0, 1, 2, 3, 4, 5],
"attention_mask": [1, 1, 1, 1, 1, 1],
"labels": [q, q, 2, 3, 4, 5],
},
{
"input_ids": [6, 7],
"attention_mask": [1, 1],
"labels": [q, 7],
},
]
batch_input = data_collator(features)
expected_input = {
"input_ids": [
[0, 1, 2, 3, 4, 5, p, p],
[6, 7, p, p, p, p, p, p],
],
"attention_mask": [
[1, 1, 1, 1, 1, 1, 0, 0],
[1, 1, 0, 0, 0, 0, 0, 0],
],
"labels": [
[q, q, 2, 3, 4, 5, q, q],
[q, 7, q, q, q, q, q, q],
],
}
for k in batch_input.keys():
assert batch_input[k].eq(torch.tensor(expected_input[k])).all()
@pytest.mark.runs_on(["cpu", "mps"])
def test_multimodal_collator():
model_args, data_args, *_ = get_infer_args(
{"model_name_or_path": "Qwen/Qwen2-VL-2B-Instruct", "template": "qwen2_vl"}
)
tokenizer_module = load_tokenizer(model_args)
template = get_template_and_fix_tokenizer(tokenizer_module["tokenizer"], data_args)
config = AutoConfig.from_pretrained(model_args.model_name_or_path)
with torch.device("meta"):
model = AutoModelForVision2Seq.from_config(config)
data_collator = MultiModalDataCollatorForSeq2Seq(
template=template,
model=model,
pad_to_multiple_of=4,
label_pad_token_id=IGNORE_INDEX,
**tokenizer_module,
)
p = tokenizer_module["tokenizer"].pad_token_id
q = IGNORE_INDEX
s = tokenizer_module["tokenizer"].convert_tokens_to_ids("<|vision_start|>")
e = tokenizer_module["tokenizer"].convert_tokens_to_ids("<|vision_end|>")
m = tokenizer_module["tokenizer"].convert_tokens_to_ids("<|image_pad|>")
fake_image = Image.new("RGB", (64, 64), (255, 255, 255))
features = [
{
"input_ids": [0, 1, 2, 3],
"attention_mask": [1, 1, 1, 1],
"labels": [0, 1, 2, 3],
},
]
batch_input = data_collator(features)
expected_input = {
"input_ids": [
[0, 1, 2, 3, s, m, m, m, m, e, p, p],
],
"attention_mask": [
[1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],
],
"labels": [
[0, 1, 2, 3, q, q, q, q, q, q, q, q],
],
"position_ids": [
[[0, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1]],
[[0, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1]],
[[0, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1]],
],
"rope_deltas": [[-8]],
**tokenizer_module["processor"].image_processor(fake_image),
}
assert batch_input.keys() == expected_input.keys()
for k in batch_input.keys():
assert batch_input[k].eq(torch.tensor(expected_input[k])).all()
@pytest.mark.runs_on(["cpu"])
def test_4d_attention_mask():
o = 0.0
x = torch.finfo(torch.float16).min
attention_mask_with_indices = torch.tensor(
[
[1, 1, 2, 2, 2, 0],
[1, 2, 2, 3, 3, 3],
]
)
attention_mask_computed = prepare_4d_attention_mask(attention_mask_with_indices, torch.float16)
attention_mask_expected = torch.tensor(
[
[
[
[o, x, x, x, x, x],
[o, o, x, x, x, x],
[x, x, o, x, x, x],
[x, x, o, o, x, x],
[x, x, o, o, o, x],
[x, x, x, x, x, x],
]
],
[
[
[o, x, x, x, x, x],
[x, o, x, x, x, x],
[x, o, o, x, x, x],
[x, x, x, o, x, x],
[x, x, x, o, o, x],
[x, x, x, o, o, o],
]
],
],
dtype=torch.float16,
)
assert list(attention_mask_computed.size()) == [2, 1, 6, 6]
assert torch.all(attention_mask_computed == attention_mask_expected)
if __name__ == "__main__":
test_multimodal_collator()
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/data/test_formatter.py | tests/data/test_formatter.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
from datetime import datetime
import pytest
from llamafactory.data.formatter import EmptyFormatter, FunctionFormatter, StringFormatter, ToolFormatter
FUNCTION = {"name": "tool_name", "arguments": {"foo": "bar", "size": 10}}
TOOLS = [
{
"name": "test_tool",
"description": "tool_desc",
"parameters": {
"type": "object",
"properties": {
"foo": {"type": "string", "description": "foo_desc"},
"bar": {"type": "number", "description": "bar_desc"},
},
"required": ["foo"],
},
}
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_empty_formatter():
formatter = EmptyFormatter(slots=["\n"])
assert formatter.apply() == ["\n"]
@pytest.mark.runs_on(["cpu", "mps"])
def test_string_formatter():
formatter = StringFormatter(slots=["<s>", "Human: {{content}}\nAssistant:"])
assert formatter.apply(content="Hi") == ["<s>", "Human: Hi\nAssistant:"]
@pytest.mark.runs_on(["cpu", "mps"])
def test_function_formatter():
formatter = FunctionFormatter(slots=["{{content}}", "</s>"], tool_format="default")
tool_calls = json.dumps(FUNCTION)
assert formatter.apply(content=tool_calls) == [
"""Action: tool_name\nAction Input: {"foo": "bar", "size": 10}""",
"</s>",
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_multi_function_formatter():
formatter = FunctionFormatter(slots=["{{content}}", "</s>"], tool_format="default")
tool_calls = json.dumps([FUNCTION] * 2)
assert formatter.apply(content=tool_calls) == [
"""Action: tool_name\nAction Input: {"foo": "bar", "size": 10}\n"""
"""Action: tool_name\nAction Input: {"foo": "bar", "size": 10}""",
"</s>",
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_default_tool_formatter():
formatter = ToolFormatter(tool_format="default")
assert formatter.apply(content=json.dumps(TOOLS)) == [
"You have access to the following tools:\n"
"> Tool Name: test_tool\n"
"Tool Description: tool_desc\n"
"Tool Args:\n"
" - foo (string, required): foo_desc\n"
" - bar (number): bar_desc\n\n"
"Use the following format if using a tool:\n"
"```\n"
"Action: tool name (one of [test_tool])\n"
"Action Input: the input to the tool, in a JSON format representing the kwargs "
"""(e.g. ```{"input": "hello world", "num_beams": 5}```)\n"""
"```\n"
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_default_tool_extractor():
formatter = ToolFormatter(tool_format="default")
result = """Action: test_tool\nAction Input: {"foo": "bar", "size": 10}"""
assert formatter.extract(result) == [("test_tool", """{"foo": "bar", "size": 10}""")]
@pytest.mark.runs_on(["cpu", "mps"])
def test_default_multi_tool_extractor():
formatter = ToolFormatter(tool_format="default")
result = (
"""Action: test_tool\nAction Input: {"foo": "bar", "size": 10}\n"""
"""Action: another_tool\nAction Input: {"foo": "job", "size": 2}"""
)
assert formatter.extract(result) == [
("test_tool", """{"foo": "bar", "size": 10}"""),
("another_tool", """{"foo": "job", "size": 2}"""),
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_glm4_function_formatter():
formatter = FunctionFormatter(slots=["{{content}}"], tool_format="glm4")
tool_calls = json.dumps(FUNCTION)
assert formatter.apply(content=tool_calls) == ["""tool_name\n{"foo": "bar", "size": 10}"""]
@pytest.mark.runs_on(["cpu", "mps"])
def test_glm4_tool_formatter():
formatter = ToolFormatter(tool_format="glm4")
assert formatter.apply(content=json.dumps(TOOLS)) == [
"你是一个名为 ChatGLM 的人工智能助手。你是基于智谱 AI 公司训练的语言模型 GLM-4 模型开发的，"
"你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具\n\n"
f"## test_tool\n\n{json.dumps(TOOLS[0], indent=4, ensure_ascii=False)}\n"
"在调用上述函数时，请使用 Json 格式表示调用的参数。"
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_glm4_tool_extractor():
formatter = ToolFormatter(tool_format="glm4")
result = """test_tool\n{"foo": "bar", "size": 10}\n"""
assert formatter.extract(result) == [("test_tool", """{"foo": "bar", "size": 10}""")]
@pytest.mark.runs_on(["cpu", "mps"])
def test_llama3_function_formatter():
formatter = FunctionFormatter(slots=["{{content}}<|eot_id|>"], tool_format="llama3")
tool_calls = json.dumps(FUNCTION)
assert formatter.apply(content=tool_calls) == [
"""{"name": "tool_name", "parameters": {"foo": "bar", "size": 10}}<|eot_id|>"""
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_llama3_multi_function_formatter():
formatter = FunctionFormatter(slots=["{{content}}<|eot_id|>"], tool_format="llama3")
tool_calls = json.dumps([FUNCTION] * 2)
assert formatter.apply(content=tool_calls) == [
"""[{"name": "tool_name", "parameters": {"foo": "bar", "size": 10}}, """
"""{"name": "tool_name", "parameters": {"foo": "bar", "size": 10}}]"""
"""<|eot_id|>"""
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_llama3_tool_formatter():
formatter = ToolFormatter(tool_format="llama3")
date = datetime.now().strftime("%d %b %Y")
wrapped_tool = {"type": "function", "function": TOOLS[0]}
assert formatter.apply(content=json.dumps(TOOLS)) == [
f"Cutting Knowledge Date: December 2023\nToday Date: {date}\n\n"
"You have access to the following functions. "
"To call a function, please respond with JSON for a function call. "
"""Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}. """
f"Do not use variables.\n\n{json.dumps(wrapped_tool, indent=4, ensure_ascii=False)}\n\n"
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_llama3_tool_extractor():
formatter = ToolFormatter(tool_format="llama3")
result = """{"name": "test_tool", "parameters": {"foo": "bar", "size": 10}}\n"""
assert formatter.extract(result) == [("test_tool", """{"foo": "bar", "size": 10}""")]
@pytest.mark.runs_on(["cpu", "mps"])
def test_llama3_multi_tool_extractor():
formatter = ToolFormatter(tool_format="llama3")
result = (
"""[{"name": "test_tool", "parameters": {"foo": "bar", "size": 10}}, """
"""{"name": "another_tool", "parameters": {"foo": "job", "size": 2}}]"""
)
assert formatter.extract(result) == [
("test_tool", """{"foo": "bar", "size": 10}"""),
("another_tool", """{"foo": "job", "size": 2}"""),
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_mistral_function_formatter():
formatter = FunctionFormatter(slots=["[TOOL_CALLS] {{content}}", "</s>"], tool_format="mistral")
tool_calls = json.dumps(FUNCTION)
assert formatter.apply(content=tool_calls) == [
"[TOOL_CALLS] " """[{"name": "tool_name", "arguments": {"foo": "bar", "size": 10}}]""",
"</s>",
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_mistral_multi_function_formatter():
formatter = FunctionFormatter(slots=["[TOOL_CALLS] {{content}}", "</s>"], tool_format="mistral")
tool_calls = json.dumps([FUNCTION] * 2)
assert formatter.apply(content=tool_calls) == [
"[TOOL_CALLS] "
"""[{"name": "tool_name", "arguments": {"foo": "bar", "size": 10}}, """
"""{"name": "tool_name", "arguments": {"foo": "bar", "size": 10}}]""",
"</s>",
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_mistral_tool_formatter():
formatter = ToolFormatter(tool_format="mistral")
wrapped_tool = {"type": "function", "function": TOOLS[0]}
assert formatter.apply(content=json.dumps(TOOLS)) == [
"[AVAILABLE_TOOLS] " + json.dumps([wrapped_tool], ensure_ascii=False) + "[/AVAILABLE_TOOLS]"
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_mistral_tool_extractor():
formatter = ToolFormatter(tool_format="mistral")
result = """{"name": "test_tool", "arguments": {"foo": "bar", "size": 10}}"""
assert formatter.extract(result) == [("test_tool", """{"foo": "bar", "size": 10}""")]
@pytest.mark.runs_on(["cpu", "mps"])
def test_mistral_multi_tool_extractor():
formatter = ToolFormatter(tool_format="mistral")
result = (
"""[{"name": "test_tool", "arguments": {"foo": "bar", "size": 10}}, """
"""{"name": "another_tool", "arguments": {"foo": "job", "size": 2}}]"""
)
assert formatter.extract(result) == [
("test_tool", """{"foo": "bar", "size": 10}"""),
("another_tool", """{"foo": "job", "size": 2}"""),
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_qwen_function_formatter():
formatter = FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen")
tool_calls = json.dumps(FUNCTION)
assert formatter.apply(content=tool_calls) == [
"""<tool_call>\n{"name": "tool_name", "arguments": {"foo": "bar", "size": 10}}\n</tool_call><|im_end|>\n"""
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_qwen_multi_function_formatter():
formatter = FunctionFormatter(slots=["{{content}}<|im_end|>\n"], tool_format="qwen")
tool_calls = json.dumps([FUNCTION] * 2)
assert formatter.apply(content=tool_calls) == [
"""<tool_call>\n{"name": "tool_name", "arguments": {"foo": "bar", "size": 10}}\n</tool_call>\n"""
"""<tool_call>\n{"name": "tool_name", "arguments": {"foo": "bar", "size": 10}}\n</tool_call>"""
"<|im_end|>\n"
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_qwen_tool_formatter():
formatter = ToolFormatter(tool_format="qwen")
wrapped_tool = {"type": "function", "function": TOOLS[0]}
assert formatter.apply(content=json.dumps(TOOLS)) == [
"\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\n"
"You are provided with function signatures within <tools></tools> XML tags:\n<tools>"
f"\n{json.dumps(wrapped_tool, ensure_ascii=False)}"
"\n</tools>\n\nFor each function call, return a json object with function name and arguments within "
"""<tool_call></tool_call> XML tags:\n<tool_call>\n{"name": <function-name>, """
""""arguments": <args-json-object>}\n</tool_call>"""
]
@pytest.mark.runs_on(["cpu", "mps"])
def test_qwen_tool_extractor():
formatter = ToolFormatter(tool_format="qwen")
result = """<tool_call>\n{"name": "test_tool", "arguments": {"foo": "bar", "size": 10}}\n</tool_call>"""
assert formatter.extract(result) == [("test_tool", """{"foo": "bar", "size": 10}""")]
@pytest.mark.runs_on(["cpu", "mps"])
def test_qwen_multi_tool_extractor():
formatter = ToolFormatter(tool_format="qwen")
result = (
"""<tool_call>\n{"name": "test_tool", "arguments": {"foo": "bar", "size": 10}}\n</tool_call>\n"""
"""<tool_call>\n{"name": "another_tool", "arguments": {"foo": "job", "size": 2}}\n</tool_call>"""
)
assert formatter.extract(result) == [
("test_tool", """{"foo": "bar", "size": 10}"""),
("another_tool", """{"foo": "job", "size": 2}"""),
]
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/data/processor/test_processor_utils.py | tests/data/processor/test_processor_utils.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytest
from llamafactory.data.processor.processor_utils import infer_seqlen
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize(
"test_input,test_output",
[
((3000, 2000, 1000), (600, 400)),
((2000, 3000, 1000), (400, 600)),
((1000, 100, 1000), (900, 100)),
((100, 1000, 1000), (100, 900)),
((100, 500, 1000), (100, 500)),
((500, 100, 1000), (500, 100)),
((10, 10, 1000), (10, 10)),
],
)
def test_infer_seqlen(test_input: tuple[int, int, int], test_output: tuple[int, int]):
assert test_output == infer_seqlen(*test_input)
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/data/processor/test_unsupervised.py | tests/data/processor/test_unsupervised.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import random
import pytest
from datasets import load_dataset
from transformers import AutoTokenizer
from llamafactory.train.test_utils import load_dataset_module
DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TINY_DATA = os.getenv("TINY_DATA", "llamafactory/tiny-supervised-dataset")
TRAIN_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"stage": "ppo",
"do_train": True,
"finetuning_type": "full",
"reward_model": "",
"reward_model_type": "full",
"dataset": "system_chat",
"dataset_dir": "REMOTE:" + DEMO_DATA,
"template": "llama3",
"cutoff_len": 8192,
"output_dir": "dummy_dir",
"overwrite_output_dir": True,
"fp16": True,
"report_to": "none", # transfromers compatibility
}
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("num_samples", [16])
def test_unsupervised_data(num_samples: int):
train_dataset = load_dataset_module(**TRAIN_ARGS)["train_dataset"]
ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3)
original_data = load_dataset(DEMO_DATA, name="system_chat", split="train")
indexes = random.choices(range(len(original_data)), k=num_samples)
for index in indexes:
messages = original_data["messages"][index]
ref_ids = ref_tokenizer.apply_chat_template(messages)
ref_input_ids = ref_tokenizer.apply_chat_template(messages[:-1], add_generation_prompt=True)
ref_labels = ref_ids[len(ref_input_ids) :]
assert train_dataset["input_ids"][index] == ref_input_ids
assert train_dataset["labels"][index] == ref_labels
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/data/processor/test_supervised.py | tests/data/processor/test_supervised.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import random
import pytest
from datasets import load_dataset
from transformers import AutoTokenizer
from llamafactory.extras.constants import IGNORE_INDEX
from llamafactory.train.test_utils import load_dataset_module
DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TINY_DATA = os.getenv("TINY_DATA", "llamafactory/tiny-supervised-dataset")
TRAIN_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"stage": "sft",
"do_train": True,
"finetuning_type": "full",
"template": "llama3",
"cutoff_len": 8192,
"output_dir": "dummy_dir",
"overwrite_output_dir": True,
"fp16": True,
}
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("num_samples", [16])
def test_supervised_single_turn(num_samples: int):
train_dataset = load_dataset_module(dataset_dir="ONLINE", dataset=TINY_DATA, **TRAIN_ARGS)["train_dataset"]
ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3)
original_data = load_dataset(TINY_DATA, split="train")
indexes = random.choices(range(len(original_data)), k=num_samples)
for index in indexes:
prompt = original_data["instruction"][index]
if original_data["input"][index]:
prompt += "\n" + original_data["input"][index]
messages = [
{"role": "user", "content": prompt},
{"role": "assistant", "content": original_data["output"][index]},
]
ref_input_ids = ref_tokenizer.apply_chat_template(messages)
assert train_dataset["input_ids"][index] == ref_input_ids
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("num_samples", [8])
def test_supervised_multi_turn(num_samples: int):
train_dataset = load_dataset_module(dataset_dir="REMOTE:" + DEMO_DATA, dataset="system_chat", **TRAIN_ARGS)[
"train_dataset"
]
ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3)
original_data = load_dataset(DEMO_DATA, name="system_chat", split="train")
indexes = random.choices(range(len(original_data)), k=num_samples)
for index in indexes:
ref_input_ids = ref_tokenizer.apply_chat_template(original_data["messages"][index])
assert train_dataset["input_ids"][index] == ref_input_ids
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("num_samples", [4])
def test_supervised_train_on_prompt(num_samples: int):
train_dataset = load_dataset_module(
dataset_dir="REMOTE:" + DEMO_DATA, dataset="system_chat", train_on_prompt=True, **TRAIN_ARGS
)["train_dataset"]
ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3)
original_data = load_dataset(DEMO_DATA, name="system_chat", split="train")
indexes = random.choices(range(len(original_data)), k=num_samples)
for index in indexes:
ref_ids = ref_tokenizer.apply_chat_template(original_data["messages"][index])
assert train_dataset["input_ids"][index] == ref_ids
assert train_dataset["labels"][index] == ref_ids
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("num_samples", [4])
def test_supervised_mask_history(num_samples: int):
train_dataset = load_dataset_module(
dataset_dir="REMOTE:" + DEMO_DATA, dataset="system_chat", mask_history=True, **TRAIN_ARGS
)["train_dataset"]
ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3)
original_data = load_dataset(DEMO_DATA, name="system_chat", split="train")
indexes = random.choices(range(len(original_data)), k=num_samples)
for index in indexes:
messages = original_data["messages"][index]
ref_input_ids = ref_tokenizer.apply_chat_template(messages)
prompt_len = len(ref_tokenizer.apply_chat_template(messages[:-1], add_generation_prompt=True))
ref_label_ids = [IGNORE_INDEX] * prompt_len + ref_input_ids[prompt_len:]
assert train_dataset["input_ids"][index] == ref_input_ids
assert train_dataset["labels"][index] == ref_label_ids
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/data/processor/test_feedback.py | tests/data/processor/test_feedback.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import random
import pytest
from datasets import load_dataset
from transformers import AutoTokenizer
from llamafactory.extras.constants import IGNORE_INDEX
from llamafactory.train.test_utils import load_dataset_module
DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TRAIN_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"stage": "kto",
"do_train": True,
"finetuning_type": "full",
"dataset": "kto_en_demo",
"dataset_dir": "REMOTE:" + DEMO_DATA,
"template": "llama3",
"cutoff_len": 8192,
"output_dir": "dummy_dir",
"overwrite_output_dir": True,
"fp16": True,
}
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("num_samples", [16])
def test_feedback_data(num_samples: int):
train_dataset = load_dataset_module(**TRAIN_ARGS)["train_dataset"]
ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3)
original_data = load_dataset(DEMO_DATA, name="kto_en_demo", split="train")
indexes = random.choices(range(len(original_data)), k=num_samples)
for index in indexes:
messages = original_data["messages"][index]
ref_input_ids = ref_tokenizer.apply_chat_template(messages)
prompt_len = len(ref_tokenizer.apply_chat_template(messages[:-1], add_generation_prompt=True))
ref_labels = [IGNORE_INDEX] * prompt_len + ref_input_ids[prompt_len:]
assert train_dataset["input_ids"][index] == ref_input_ids
assert train_dataset["labels"][index] == ref_labels
assert train_dataset["kto_tags"][index] == original_data["label"][index]
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
hiyouga/LlamaFactory | https://github.com/hiyouga/LlamaFactory/blob/f60a6e3d015962198b7c626936f117e83260bde9/tests/data/processor/test_pairwise.py | tests/data/processor/test_pairwise.py | # Copyright 2025 the LlamaFactory team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import random
import pytest
from datasets import load_dataset
from transformers import AutoTokenizer
from llamafactory.extras.constants import IGNORE_INDEX
from llamafactory.train.test_utils import load_dataset_module
DEMO_DATA = os.getenv("DEMO_DATA", "llamafactory/demo_data")
TINY_LLAMA3 = os.getenv("TINY_LLAMA3", "llamafactory/tiny-random-Llama-3")
TRAIN_ARGS = {
"model_name_or_path": TINY_LLAMA3,
"stage": "rm",
"do_train": True,
"finetuning_type": "full",
"dataset": "dpo_en_demo",
"dataset_dir": "REMOTE:" + DEMO_DATA,
"template": "llama3",
"cutoff_len": 8192,
"output_dir": "dummy_dir",
"overwrite_output_dir": True,
"fp16": True,
}
def _convert_sharegpt_to_openai(messages: list[dict[str, str]]) -> list[dict[str, str]]:
role_mapping = {"human": "user", "gpt": "assistant", "system": "system"}
new_messages = []
for message in messages:
new_messages.append({"role": role_mapping[message["from"]], "content": message["value"]})
return new_messages
@pytest.mark.runs_on(["cpu", "mps"])
@pytest.mark.parametrize("num_samples", [16])
def test_pairwise_data(num_samples: int):
train_dataset = load_dataset_module(**TRAIN_ARGS)["train_dataset"]
ref_tokenizer = AutoTokenizer.from_pretrained(TINY_LLAMA3)
original_data = load_dataset(DEMO_DATA, name="dpo_en_demo", split="train")
indexes = random.choices(range(len(original_data)), k=num_samples)
for index in indexes:
chosen_messages = original_data["conversations"][index] + [original_data["chosen"][index]]
rejected_messages = original_data["conversations"][index] + [original_data["rejected"][index]]
chosen_messages = _convert_sharegpt_to_openai(chosen_messages)
rejected_messages = _convert_sharegpt_to_openai(rejected_messages)
ref_chosen_input_ids = ref_tokenizer.apply_chat_template(chosen_messages)
chosen_prompt_len = len(ref_tokenizer.apply_chat_template(chosen_messages[:-1], add_generation_prompt=True))
ref_chosen_labels = [IGNORE_INDEX] * chosen_prompt_len + ref_chosen_input_ids[chosen_prompt_len:]
ref_rejected_input_ids = ref_tokenizer.apply_chat_template(rejected_messages)
rejected_prompt_len = len(
ref_tokenizer.apply_chat_template(rejected_messages[:-1], add_generation_prompt=True)
)
ref_rejected_labels = [IGNORE_INDEX] * rejected_prompt_len + ref_rejected_input_ids[rejected_prompt_len:]
assert train_dataset["chosen_input_ids"][index] == ref_chosen_input_ids
assert train_dataset["chosen_labels"][index] == ref_chosen_labels
assert train_dataset["rejected_input_ids"][index] == ref_rejected_input_ids
assert train_dataset["rejected_labels"][index] == ref_rejected_labels
| python | Apache-2.0 | f60a6e3d015962198b7c626936f117e83260bde9 | 2026-01-04T14:38:25.134237Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/setup.py | setup.py | #!/usr/bin/env python
# coding: utf-8
from __future__ import print_function
import os.path
import warnings
import sys
try:
from setuptools import setup, Command
setuptools_available = True
except ImportError:
from distutils.core import setup, Command
setuptools_available = False
from distutils.spawn import spawn
try:
# This will create an exe that needs Microsoft Visual C++ 2008
# Redistributable Package
import py2exe
except ImportError:
if len(sys.argv) >= 2 and sys.argv[1] == 'py2exe':
print('Cannot import py2exe', file=sys.stderr)
exit(1)
py2exe_options = {
'bundle_files': 1,
'compressed': 1,
'optimize': 2,
'dist_dir': '.',
'dll_excludes': ['w9xpopen.exe', 'crypt32.dll'],
}
# Get the version from youtube_dl/version.py without importing the package
exec(compile(open('youtube_dl/version.py').read(),
'youtube_dl/version.py', 'exec'))
DESCRIPTION = 'YouTube video downloader'
LONG_DESCRIPTION = 'Command-line program to download videos from YouTube.com and other video sites'
py2exe_console = [{
'script': './youtube_dl/__main__.py',
'dest_base': 'youtube-dl',
'version': __version__,
'description': DESCRIPTION,
'comments': LONG_DESCRIPTION,
'product_name': 'youtube-dl',
'product_version': __version__,
}]
py2exe_params = {
'console': py2exe_console,
'options': {'py2exe': py2exe_options},
'zipfile': None
}
if len(sys.argv) >= 2 and sys.argv[1] == 'py2exe':
params = py2exe_params
else:
files_spec = [
('etc/bash_completion.d', ['youtube-dl.bash-completion']),
('etc/fish/completions', ['youtube-dl.fish']),
('share/doc/youtube_dl', ['README.txt']),
('share/man/man1', ['youtube-dl.1'])
]
root = os.path.dirname(os.path.abspath(__file__))
data_files = []
for dirname, files in files_spec:
resfiles = []
for fn in files:
if not os.path.exists(fn):
warnings.warn('Skipping file %s since it is not present. Type make to build all automatically generated files.' % fn)
else:
resfiles.append(fn)
data_files.append((dirname, resfiles))
params = {
'data_files': data_files,
}
if setuptools_available:
params['entry_points'] = {'console_scripts': ['youtube-dl = youtube_dl:main']}
else:
params['scripts'] = ['bin/youtube-dl']
class build_lazy_extractors(Command):
description = 'Build the extractor lazy loading module'
user_options = []
def initialize_options(self):
pass
def finalize_options(self):
pass
def run(self):
spawn(
[sys.executable, 'devscripts/make_lazy_extractors.py', 'youtube_dl/extractor/lazy_extractors.py'],
dry_run=self.dry_run,
)
setup(
name='youtube_dl',
version=__version__,
description=DESCRIPTION,
long_description=LONG_DESCRIPTION,
url='https://github.com/ytdl-org/youtube-dl',
author='Ricardo Garcia',
author_email='ytdl@yt-dl.org',
maintainer='Sergey M.',
maintainer_email='dstftw@gmail.com',
license='Unlicense',
packages=[
'youtube_dl',
'youtube_dl.extractor', 'youtube_dl.downloader',
'youtube_dl.postprocessor'],
# Provokes warning on most systems (why?!)
# test_suite = 'nose.collector',
# test_requires = ['nosetest'],
classifiers=[
'Topic :: Multimedia :: Video',
'Development Status :: 5 - Production/Stable',
'Environment :: Console',
'License :: Public Domain',
'Programming Language :: Python',
'Programming Language :: Python :: 2',
'Programming Language :: Python :: 2.6',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.2',
'Programming Language :: Python :: 3.3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Programming Language :: Python :: 3.7',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: Implementation',
'Programming Language :: Python :: Implementation :: CPython',
'Programming Language :: Python :: Implementation :: IronPython',
'Programming Language :: Python :: Implementation :: Jython',
'Programming Language :: Python :: Implementation :: PyPy',
],
cmdclass={'build_lazy_extractors': build_lazy_extractors},
**params
)
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/compat.py | youtube_dl/compat.py | # coding: utf-8
from __future__ import unicode_literals
from __future__ import division
import base64
import binascii
import collections
import ctypes
import datetime
import email
import getpass
import io
import itertools
import optparse
import os
import platform
import re
import shlex
import socket
import struct
import subprocess
import sys
import types
import xml.etree.ElementTree
_IDENTITY = lambda x: x
# naming convention
# 'compat_' + Python3_name.replace('.', '_')
# other aliases exist for convenience and/or legacy
# wrap disposable test values in type() to reclaim storage
# deal with critical unicode/str things first:
# compat_str, compat_basestring, compat_chr
try:
# Python 2
compat_str, compat_basestring, compat_chr = (
unicode, basestring, unichr
)
except NameError:
compat_str, compat_basestring, compat_chr = (
str, (str, bytes), chr
)
# compat_casefold
try:
compat_str.casefold
compat_casefold = lambda s: s.casefold()
except AttributeError:
from .casefold import _casefold as compat_casefold
# compat_collections_abc
try:
import collections.abc as compat_collections_abc
except ImportError:
compat_collections_abc = collections
# compat_urllib_request
try:
import urllib.request as compat_urllib_request
except ImportError: # Python 2
import urllib2 as compat_urllib_request
# Also fix up lack of method arg in old Pythons
try:
type(compat_urllib_request.Request('http://127.0.0.1', method='GET'))
except TypeError:
def _add_init_method_arg(cls):
init = cls.__init__
def wrapped_init(self, *args, **kwargs):
method = kwargs.pop('method', 'GET')
init(self, *args, **kwargs)
if any(callable(x.__dict__.get('get_method')) for x in (self.__class__, self) if x != cls):
# allow instance or its subclass to override get_method()
return
if self.has_data() and method == 'GET':
method = 'POST'
self.get_method = types.MethodType(lambda _: method, self)
cls.__init__ = wrapped_init
_add_init_method_arg(compat_urllib_request.Request)
del _add_init_method_arg
# compat_urllib_error
try:
import urllib.error as compat_urllib_error
except ImportError: # Python 2
import urllib2 as compat_urllib_error
# compat_urllib_parse
try:
import urllib.parse as compat_urllib_parse
except ImportError: # Python 2
import urllib as compat_urllib_parse
import urlparse as _urlparse
for a in dir(_urlparse):
if not hasattr(compat_urllib_parse, a):
setattr(compat_urllib_parse, a, getattr(_urlparse, a))
del _urlparse
# unfavoured aliases
compat_urlparse = compat_urllib_parse
compat_urllib_parse_urlparse = compat_urllib_parse.urlparse
# compat_urllib_response
try:
import urllib.response as compat_urllib_response
except ImportError: # Python 2
import urllib as compat_urllib_response
# compat_urllib_response.addinfourl
try:
compat_urllib_response.addinfourl.status
except AttributeError:
# .getcode() is deprecated in Py 3.
compat_urllib_response.addinfourl.status = property(lambda self: self.getcode())
# compat_http_cookiejar
try:
import http.cookiejar as compat_cookiejar
except ImportError: # Python 2
import cookielib as compat_cookiejar
compat_http_cookiejar = compat_cookiejar
if sys.version_info[0] == 2:
class compat_cookiejar_Cookie(compat_cookiejar.Cookie):
def __init__(self, version, name, value, *args, **kwargs):
if isinstance(name, compat_str):
name = name.encode()
if isinstance(value, compat_str):
value = value.encode()
compat_cookiejar.Cookie.__init__(self, version, name, value, *args, **kwargs)
else:
compat_cookiejar_Cookie = compat_cookiejar.Cookie
compat_http_cookiejar_Cookie = compat_cookiejar_Cookie
# compat_http_cookies
try:
import http.cookies as compat_cookies
except ImportError: # Python 2
import Cookie as compat_cookies
compat_http_cookies = compat_cookies
# compat_http_cookies_SimpleCookie
if sys.version_info[0] == 2 or sys.version_info < (3, 3):
class compat_cookies_SimpleCookie(compat_cookies.SimpleCookie):
def load(self, rawdata):
must_have_value = 0
if not isinstance(rawdata, dict):
if sys.version_info[:2] != (2, 7) or sys.platform.startswith('java'):
# attribute must have value for parsing
rawdata, must_have_value = re.subn(
r'(?i)(;\s*)(secure|httponly)(\s*(?:;|$))', r'\1\2=\2\3', rawdata)
if sys.version_info[0] == 2:
if isinstance(rawdata, compat_str):
rawdata = str(rawdata)
super(compat_cookies_SimpleCookie, self).load(rawdata)
if must_have_value > 0:
for morsel in self.values():
for attr in ('secure', 'httponly'):
if morsel.get(attr):
morsel[attr] = True
else:
compat_cookies_SimpleCookie = compat_cookies.SimpleCookie
compat_http_cookies_SimpleCookie = compat_cookies_SimpleCookie
# compat_html_entities, probably useless now
try:
import html.entities as compat_html_entities
except ImportError: # Python 2
import htmlentitydefs as compat_html_entities
# compat_html_entities_html5
try: # Python >= 3.3
compat_html_entities_html5 = compat_html_entities.html5
except AttributeError:
# Copied from CPython 3.5.1 html/entities.py
compat_html_entities_html5 = {
'Aacute': '\xc1',
'aacute': '\xe1',
'Aacute;': '\xc1',
'aacute;': '\xe1',
'Abreve;': '\u0102',
'abreve;': '\u0103',
'ac;': '\u223e',
'acd;': '\u223f',
'acE;': '\u223e\u0333',
'Acirc': '\xc2',
'acirc': '\xe2',
'Acirc;': '\xc2',
'acirc;': '\xe2',
'acute': '\xb4',
'acute;': '\xb4',
'Acy;': '\u0410',
'acy;': '\u0430',
'AElig': '\xc6',
'aelig': '\xe6',
'AElig;': '\xc6',
'aelig;': '\xe6',
'af;': '\u2061',
'Afr;': '\U0001d504',
'afr;': '\U0001d51e',
'Agrave': '\xc0',
'agrave': '\xe0',
'Agrave;': '\xc0',
'agrave;': '\xe0',
'alefsym;': '\u2135',
'aleph;': '\u2135',
'Alpha;': '\u0391',
'alpha;': '\u03b1',
'Amacr;': '\u0100',
'amacr;': '\u0101',
'amalg;': '\u2a3f',
'AMP': '&',
'amp': '&',
'AMP;': '&',
'amp;': '&',
'And;': '\u2a53',
'and;': '\u2227',
'andand;': '\u2a55',
'andd;': '\u2a5c',
'andslope;': '\u2a58',
'andv;': '\u2a5a',
'ang;': '\u2220',
'ange;': '\u29a4',
'angle;': '\u2220',
'angmsd;': '\u2221',
'angmsdaa;': '\u29a8',
'angmsdab;': '\u29a9',
'angmsdac;': '\u29aa',
'angmsdad;': '\u29ab',
'angmsdae;': '\u29ac',
'angmsdaf;': '\u29ad',
'angmsdag;': '\u29ae',
'angmsdah;': '\u29af',
'angrt;': '\u221f',
'angrtvb;': '\u22be',
'angrtvbd;': '\u299d',
'angsph;': '\u2222',
'angst;': '\xc5',
'angzarr;': '\u237c',
'Aogon;': '\u0104',
'aogon;': '\u0105',
'Aopf;': '\U0001d538',
'aopf;': '\U0001d552',
'ap;': '\u2248',
'apacir;': '\u2a6f',
'apE;': '\u2a70',
'ape;': '\u224a',
'apid;': '\u224b',
'apos;': "'",
'ApplyFunction;': '\u2061',
'approx;': '\u2248',
'approxeq;': '\u224a',
'Aring': '\xc5',
'aring': '\xe5',
'Aring;': '\xc5',
'aring;': '\xe5',
'Ascr;': '\U0001d49c',
'ascr;': '\U0001d4b6',
'Assign;': '\u2254',
'ast;': '*',
'asymp;': '\u2248',
'asympeq;': '\u224d',
'Atilde': '\xc3',
'atilde': '\xe3',
'Atilde;': '\xc3',
'atilde;': '\xe3',
'Auml': '\xc4',
'auml': '\xe4',
'Auml;': '\xc4',
'auml;': '\xe4',
'awconint;': '\u2233',
'awint;': '\u2a11',
'backcong;': '\u224c',
'backepsilon;': '\u03f6',
'backprime;': '\u2035',
'backsim;': '\u223d',
'backsimeq;': '\u22cd',
'Backslash;': '\u2216',
'Barv;': '\u2ae7',
'barvee;': '\u22bd',
'Barwed;': '\u2306',
'barwed;': '\u2305',
'barwedge;': '\u2305',
'bbrk;': '\u23b5',
'bbrktbrk;': '\u23b6',
'bcong;': '\u224c',
'Bcy;': '\u0411',
'bcy;': '\u0431',
'bdquo;': '\u201e',
'becaus;': '\u2235',
'Because;': '\u2235',
'because;': '\u2235',
'bemptyv;': '\u29b0',
'bepsi;': '\u03f6',
'bernou;': '\u212c',
'Bernoullis;': '\u212c',
'Beta;': '\u0392',
'beta;': '\u03b2',
'beth;': '\u2136',
'between;': '\u226c',
'Bfr;': '\U0001d505',
'bfr;': '\U0001d51f',
'bigcap;': '\u22c2',
'bigcirc;': '\u25ef',
'bigcup;': '\u22c3',
'bigodot;': '\u2a00',
'bigoplus;': '\u2a01',
'bigotimes;': '\u2a02',
'bigsqcup;': '\u2a06',
'bigstar;': '\u2605',
'bigtriangledown;': '\u25bd',
'bigtriangleup;': '\u25b3',
'biguplus;': '\u2a04',
'bigvee;': '\u22c1',
'bigwedge;': '\u22c0',
'bkarow;': '\u290d',
'blacklozenge;': '\u29eb',
'blacksquare;': '\u25aa',
'blacktriangle;': '\u25b4',
'blacktriangledown;': '\u25be',
'blacktriangleleft;': '\u25c2',
'blacktriangleright;': '\u25b8',
'blank;': '\u2423',
'blk12;': '\u2592',
'blk14;': '\u2591',
'blk34;': '\u2593',
'block;': '\u2588',
'bne;': '=\u20e5',
'bnequiv;': '\u2261\u20e5',
'bNot;': '\u2aed',
'bnot;': '\u2310',
'Bopf;': '\U0001d539',
'bopf;': '\U0001d553',
'bot;': '\u22a5',
'bottom;': '\u22a5',
'bowtie;': '\u22c8',
'boxbox;': '\u29c9',
'boxDL;': '\u2557',
'boxDl;': '\u2556',
'boxdL;': '\u2555',
'boxdl;': '\u2510',
'boxDR;': '\u2554',
'boxDr;': '\u2553',
'boxdR;': '\u2552',
'boxdr;': '\u250c',
'boxH;': '\u2550',
'boxh;': '\u2500',
'boxHD;': '\u2566',
'boxHd;': '\u2564',
'boxhD;': '\u2565',
'boxhd;': '\u252c',
'boxHU;': '\u2569',
'boxHu;': '\u2567',
'boxhU;': '\u2568',
'boxhu;': '\u2534',
'boxminus;': '\u229f',
'boxplus;': '\u229e',
'boxtimes;': '\u22a0',
'boxUL;': '\u255d',
'boxUl;': '\u255c',
'boxuL;': '\u255b',
'boxul;': '\u2518',
'boxUR;': '\u255a',
'boxUr;': '\u2559',
'boxuR;': '\u2558',
'boxur;': '\u2514',
'boxV;': '\u2551',
'boxv;': '\u2502',
'boxVH;': '\u256c',
'boxVh;': '\u256b',
'boxvH;': '\u256a',
'boxvh;': '\u253c',
'boxVL;': '\u2563',
'boxVl;': '\u2562',
'boxvL;': '\u2561',
'boxvl;': '\u2524',
'boxVR;': '\u2560',
'boxVr;': '\u255f',
'boxvR;': '\u255e',
'boxvr;': '\u251c',
'bprime;': '\u2035',
'Breve;': '\u02d8',
'breve;': '\u02d8',
'brvbar': '\xa6',
'brvbar;': '\xa6',
'Bscr;': '\u212c',
'bscr;': '\U0001d4b7',
'bsemi;': '\u204f',
'bsim;': '\u223d',
'bsime;': '\u22cd',
'bsol;': '\\',
'bsolb;': '\u29c5',
'bsolhsub;': '\u27c8',
'bull;': '\u2022',
'bullet;': '\u2022',
'bump;': '\u224e',
'bumpE;': '\u2aae',
'bumpe;': '\u224f',
'Bumpeq;': '\u224e',
'bumpeq;': '\u224f',
'Cacute;': '\u0106',
'cacute;': '\u0107',
'Cap;': '\u22d2',
'cap;': '\u2229',
'capand;': '\u2a44',
'capbrcup;': '\u2a49',
'capcap;': '\u2a4b',
'capcup;': '\u2a47',
'capdot;': '\u2a40',
'CapitalDifferentialD;': '\u2145',
'caps;': '\u2229\ufe00',
'caret;': '\u2041',
'caron;': '\u02c7',
'Cayleys;': '\u212d',
'ccaps;': '\u2a4d',
'Ccaron;': '\u010c',
'ccaron;': '\u010d',
'Ccedil': '\xc7',
'ccedil': '\xe7',
'Ccedil;': '\xc7',
'ccedil;': '\xe7',
'Ccirc;': '\u0108',
'ccirc;': '\u0109',
'Cconint;': '\u2230',
'ccups;': '\u2a4c',
'ccupssm;': '\u2a50',
'Cdot;': '\u010a',
'cdot;': '\u010b',
'cedil': '\xb8',
'cedil;': '\xb8',
'Cedilla;': '\xb8',
'cemptyv;': '\u29b2',
'cent': '\xa2',
'cent;': '\xa2',
'CenterDot;': '\xb7',
'centerdot;': '\xb7',
'Cfr;': '\u212d',
'cfr;': '\U0001d520',
'CHcy;': '\u0427',
'chcy;': '\u0447',
'check;': '\u2713',
'checkmark;': '\u2713',
'Chi;': '\u03a7',
'chi;': '\u03c7',
'cir;': '\u25cb',
'circ;': '\u02c6',
'circeq;': '\u2257',
'circlearrowleft;': '\u21ba',
'circlearrowright;': '\u21bb',
'circledast;': '\u229b',
'circledcirc;': '\u229a',
'circleddash;': '\u229d',
'CircleDot;': '\u2299',
'circledR;': '\xae',
'circledS;': '\u24c8',
'CircleMinus;': '\u2296',
'CirclePlus;': '\u2295',
'CircleTimes;': '\u2297',
'cirE;': '\u29c3',
'cire;': '\u2257',
'cirfnint;': '\u2a10',
'cirmid;': '\u2aef',
'cirscir;': '\u29c2',
'ClockwiseContourIntegral;': '\u2232',
'CloseCurlyDoubleQuote;': '\u201d',
'CloseCurlyQuote;': '\u2019',
'clubs;': '\u2663',
'clubsuit;': '\u2663',
'Colon;': '\u2237',
'colon;': ':',
'Colone;': '\u2a74',
'colone;': '\u2254',
'coloneq;': '\u2254',
'comma;': ',',
'commat;': '@',
'comp;': '\u2201',
'compfn;': '\u2218',
'complement;': '\u2201',
'complexes;': '\u2102',
'cong;': '\u2245',
'congdot;': '\u2a6d',
'Congruent;': '\u2261',
'Conint;': '\u222f',
'conint;': '\u222e',
'ContourIntegral;': '\u222e',
'Copf;': '\u2102',
'copf;': '\U0001d554',
'coprod;': '\u2210',
'Coproduct;': '\u2210',
'COPY': '\xa9',
'copy': '\xa9',
'COPY;': '\xa9',
'copy;': '\xa9',
'copysr;': '\u2117',
'CounterClockwiseContourIntegral;': '\u2233',
'crarr;': '\u21b5',
'Cross;': '\u2a2f',
'cross;': '\u2717',
'Cscr;': '\U0001d49e',
'cscr;': '\U0001d4b8',
'csub;': '\u2acf',
'csube;': '\u2ad1',
'csup;': '\u2ad0',
'csupe;': '\u2ad2',
'ctdot;': '\u22ef',
'cudarrl;': '\u2938',
'cudarrr;': '\u2935',
'cuepr;': '\u22de',
'cuesc;': '\u22df',
'cularr;': '\u21b6',
'cularrp;': '\u293d',
'Cup;': '\u22d3',
'cup;': '\u222a',
'cupbrcap;': '\u2a48',
'CupCap;': '\u224d',
'cupcap;': '\u2a46',
'cupcup;': '\u2a4a',
'cupdot;': '\u228d',
'cupor;': '\u2a45',
'cups;': '\u222a\ufe00',
'curarr;': '\u21b7',
'curarrm;': '\u293c',
'curlyeqprec;': '\u22de',
'curlyeqsucc;': '\u22df',
'curlyvee;': '\u22ce',
'curlywedge;': '\u22cf',
'curren': '\xa4',
'curren;': '\xa4',
'curvearrowleft;': '\u21b6',
'curvearrowright;': '\u21b7',
'cuvee;': '\u22ce',
'cuwed;': '\u22cf',
'cwconint;': '\u2232',
'cwint;': '\u2231',
'cylcty;': '\u232d',
'Dagger;': '\u2021',
'dagger;': '\u2020',
'daleth;': '\u2138',
'Darr;': '\u21a1',
'dArr;': '\u21d3',
'darr;': '\u2193',
'dash;': '\u2010',
'Dashv;': '\u2ae4',
'dashv;': '\u22a3',
'dbkarow;': '\u290f',
'dblac;': '\u02dd',
'Dcaron;': '\u010e',
'dcaron;': '\u010f',
'Dcy;': '\u0414',
'dcy;': '\u0434',
'DD;': '\u2145',
'dd;': '\u2146',
'ddagger;': '\u2021',
'ddarr;': '\u21ca',
'DDotrahd;': '\u2911',
'ddotseq;': '\u2a77',
'deg': '\xb0',
'deg;': '\xb0',
'Del;': '\u2207',
'Delta;': '\u0394',
'delta;': '\u03b4',
'demptyv;': '\u29b1',
'dfisht;': '\u297f',
'Dfr;': '\U0001d507',
'dfr;': '\U0001d521',
'dHar;': '\u2965',
'dharl;': '\u21c3',
'dharr;': '\u21c2',
'DiacriticalAcute;': '\xb4',
'DiacriticalDot;': '\u02d9',
'DiacriticalDoubleAcute;': '\u02dd',
'DiacriticalGrave;': '`',
'DiacriticalTilde;': '\u02dc',
'diam;': '\u22c4',
'Diamond;': '\u22c4',
'diamond;': '\u22c4',
'diamondsuit;': '\u2666',
'diams;': '\u2666',
'die;': '\xa8',
'DifferentialD;': '\u2146',
'digamma;': '\u03dd',
'disin;': '\u22f2',
'div;': '\xf7',
'divide': '\xf7',
'divide;': '\xf7',
'divideontimes;': '\u22c7',
'divonx;': '\u22c7',
'DJcy;': '\u0402',
'djcy;': '\u0452',
'dlcorn;': '\u231e',
'dlcrop;': '\u230d',
'dollar;': '$',
'Dopf;': '\U0001d53b',
'dopf;': '\U0001d555',
'Dot;': '\xa8',
'dot;': '\u02d9',
'DotDot;': '\u20dc',
'doteq;': '\u2250',
'doteqdot;': '\u2251',
'DotEqual;': '\u2250',
'dotminus;': '\u2238',
'dotplus;': '\u2214',
'dotsquare;': '\u22a1',
'doublebarwedge;': '\u2306',
'DoubleContourIntegral;': '\u222f',
'DoubleDot;': '\xa8',
'DoubleDownArrow;': '\u21d3',
'DoubleLeftArrow;': '\u21d0',
'DoubleLeftRightArrow;': '\u21d4',
'DoubleLeftTee;': '\u2ae4',
'DoubleLongLeftArrow;': '\u27f8',
'DoubleLongLeftRightArrow;': '\u27fa',
'DoubleLongRightArrow;': '\u27f9',
'DoubleRightArrow;': '\u21d2',
'DoubleRightTee;': '\u22a8',
'DoubleUpArrow;': '\u21d1',
'DoubleUpDownArrow;': '\u21d5',
'DoubleVerticalBar;': '\u2225',
'DownArrow;': '\u2193',
'Downarrow;': '\u21d3',
'downarrow;': '\u2193',
'DownArrowBar;': '\u2913',
'DownArrowUpArrow;': '\u21f5',
'DownBreve;': '\u0311',
'downdownarrows;': '\u21ca',
'downharpoonleft;': '\u21c3',
'downharpoonright;': '\u21c2',
'DownLeftRightVector;': '\u2950',
'DownLeftTeeVector;': '\u295e',
'DownLeftVector;': '\u21bd',
'DownLeftVectorBar;': '\u2956',
'DownRightTeeVector;': '\u295f',
'DownRightVector;': '\u21c1',
'DownRightVectorBar;': '\u2957',
'DownTee;': '\u22a4',
'DownTeeArrow;': '\u21a7',
'drbkarow;': '\u2910',
'drcorn;': '\u231f',
'drcrop;': '\u230c',
'Dscr;': '\U0001d49f',
'dscr;': '\U0001d4b9',
'DScy;': '\u0405',
'dscy;': '\u0455',
'dsol;': '\u29f6',
'Dstrok;': '\u0110',
'dstrok;': '\u0111',
'dtdot;': '\u22f1',
'dtri;': '\u25bf',
'dtrif;': '\u25be',
'duarr;': '\u21f5',
'duhar;': '\u296f',
'dwangle;': '\u29a6',
'DZcy;': '\u040f',
'dzcy;': '\u045f',
'dzigrarr;': '\u27ff',
'Eacute': '\xc9',
'eacute': '\xe9',
'Eacute;': '\xc9',
'eacute;': '\xe9',
'easter;': '\u2a6e',
'Ecaron;': '\u011a',
'ecaron;': '\u011b',
'ecir;': '\u2256',
'Ecirc': '\xca',
'ecirc': '\xea',
'Ecirc;': '\xca',
'ecirc;': '\xea',
'ecolon;': '\u2255',
'Ecy;': '\u042d',
'ecy;': '\u044d',
'eDDot;': '\u2a77',
'Edot;': '\u0116',
'eDot;': '\u2251',
'edot;': '\u0117',
'ee;': '\u2147',
'efDot;': '\u2252',
'Efr;': '\U0001d508',
'efr;': '\U0001d522',
'eg;': '\u2a9a',
'Egrave': '\xc8',
'egrave': '\xe8',
'Egrave;': '\xc8',
'egrave;': '\xe8',
'egs;': '\u2a96',
'egsdot;': '\u2a98',
'el;': '\u2a99',
'Element;': '\u2208',
'elinters;': '\u23e7',
'ell;': '\u2113',
'els;': '\u2a95',
'elsdot;': '\u2a97',
'Emacr;': '\u0112',
'emacr;': '\u0113',
'empty;': '\u2205',
'emptyset;': '\u2205',
'EmptySmallSquare;': '\u25fb',
'emptyv;': '\u2205',
'EmptyVerySmallSquare;': '\u25ab',
'emsp13;': '\u2004',
'emsp14;': '\u2005',
'emsp;': '\u2003',
'ENG;': '\u014a',
'eng;': '\u014b',
'ensp;': '\u2002',
'Eogon;': '\u0118',
'eogon;': '\u0119',
'Eopf;': '\U0001d53c',
'eopf;': '\U0001d556',
'epar;': '\u22d5',
'eparsl;': '\u29e3',
'eplus;': '\u2a71',
'epsi;': '\u03b5',
'Epsilon;': '\u0395',
'epsilon;': '\u03b5',
'epsiv;': '\u03f5',
'eqcirc;': '\u2256',
'eqcolon;': '\u2255',
'eqsim;': '\u2242',
'eqslantgtr;': '\u2a96',
'eqslantless;': '\u2a95',
'Equal;': '\u2a75',
'equals;': '=',
'EqualTilde;': '\u2242',
'equest;': '\u225f',
'Equilibrium;': '\u21cc',
'equiv;': '\u2261',
'equivDD;': '\u2a78',
'eqvparsl;': '\u29e5',
'erarr;': '\u2971',
'erDot;': '\u2253',
'Escr;': '\u2130',
'escr;': '\u212f',
'esdot;': '\u2250',
'Esim;': '\u2a73',
'esim;': '\u2242',
'Eta;': '\u0397',
'eta;': '\u03b7',
'ETH': '\xd0',
'eth': '\xf0',
'ETH;': '\xd0',
'eth;': '\xf0',
'Euml': '\xcb',
'euml': '\xeb',
'Euml;': '\xcb',
'euml;': '\xeb',
'euro;': '\u20ac',
'excl;': '!',
'exist;': '\u2203',
'Exists;': '\u2203',
'expectation;': '\u2130',
'ExponentialE;': '\u2147',
'exponentiale;': '\u2147',
'fallingdotseq;': '\u2252',
'Fcy;': '\u0424',
'fcy;': '\u0444',
'female;': '\u2640',
'ffilig;': '\ufb03',
'fflig;': '\ufb00',
'ffllig;': '\ufb04',
'Ffr;': '\U0001d509',
'ffr;': '\U0001d523',
'filig;': '\ufb01',
'FilledSmallSquare;': '\u25fc',
'FilledVerySmallSquare;': '\u25aa',
'fjlig;': 'fj',
'flat;': '\u266d',
'fllig;': '\ufb02',
'fltns;': '\u25b1',
'fnof;': '\u0192',
'Fopf;': '\U0001d53d',
'fopf;': '\U0001d557',
'ForAll;': '\u2200',
'forall;': '\u2200',
'fork;': '\u22d4',
'forkv;': '\u2ad9',
'Fouriertrf;': '\u2131',
'fpartint;': '\u2a0d',
'frac12': '\xbd',
'frac12;': '\xbd',
'frac13;': '\u2153',
'frac14': '\xbc',
'frac14;': '\xbc',
'frac15;': '\u2155',
'frac16;': '\u2159',
'frac18;': '\u215b',
'frac23;': '\u2154',
'frac25;': '\u2156',
'frac34': '\xbe',
'frac34;': '\xbe',
'frac35;': '\u2157',
'frac38;': '\u215c',
'frac45;': '\u2158',
'frac56;': '\u215a',
'frac58;': '\u215d',
'frac78;': '\u215e',
'frasl;': '\u2044',
'frown;': '\u2322',
'Fscr;': '\u2131',
'fscr;': '\U0001d4bb',
'gacute;': '\u01f5',
'Gamma;': '\u0393',
'gamma;': '\u03b3',
'Gammad;': '\u03dc',
'gammad;': '\u03dd',
'gap;': '\u2a86',
'Gbreve;': '\u011e',
'gbreve;': '\u011f',
'Gcedil;': '\u0122',
'Gcirc;': '\u011c',
'gcirc;': '\u011d',
'Gcy;': '\u0413',
'gcy;': '\u0433',
'Gdot;': '\u0120',
'gdot;': '\u0121',
'gE;': '\u2267',
'ge;': '\u2265',
'gEl;': '\u2a8c',
'gel;': '\u22db',
'geq;': '\u2265',
'geqq;': '\u2267',
'geqslant;': '\u2a7e',
'ges;': '\u2a7e',
'gescc;': '\u2aa9',
'gesdot;': '\u2a80',
'gesdoto;': '\u2a82',
'gesdotol;': '\u2a84',
'gesl;': '\u22db\ufe00',
'gesles;': '\u2a94',
'Gfr;': '\U0001d50a',
'gfr;': '\U0001d524',
'Gg;': '\u22d9',
'gg;': '\u226b',
'ggg;': '\u22d9',
'gimel;': '\u2137',
'GJcy;': '\u0403',
'gjcy;': '\u0453',
'gl;': '\u2277',
'gla;': '\u2aa5',
'glE;': '\u2a92',
'glj;': '\u2aa4',
'gnap;': '\u2a8a',
'gnapprox;': '\u2a8a',
'gnE;': '\u2269',
'gne;': '\u2a88',
'gneq;': '\u2a88',
'gneqq;': '\u2269',
'gnsim;': '\u22e7',
'Gopf;': '\U0001d53e',
'gopf;': '\U0001d558',
'grave;': '`',
'GreaterEqual;': '\u2265',
'GreaterEqualLess;': '\u22db',
'GreaterFullEqual;': '\u2267',
'GreaterGreater;': '\u2aa2',
'GreaterLess;': '\u2277',
'GreaterSlantEqual;': '\u2a7e',
'GreaterTilde;': '\u2273',
'Gscr;': '\U0001d4a2',
'gscr;': '\u210a',
'gsim;': '\u2273',
'gsime;': '\u2a8e',
'gsiml;': '\u2a90',
'GT': '>',
'gt': '>',
'GT;': '>',
'Gt;': '\u226b',
'gt;': '>',
'gtcc;': '\u2aa7',
'gtcir;': '\u2a7a',
'gtdot;': '\u22d7',
'gtlPar;': '\u2995',
'gtquest;': '\u2a7c',
'gtrapprox;': '\u2a86',
'gtrarr;': '\u2978',
'gtrdot;': '\u22d7',
'gtreqless;': '\u22db',
'gtreqqless;': '\u2a8c',
'gtrless;': '\u2277',
'gtrsim;': '\u2273',
'gvertneqq;': '\u2269\ufe00',
'gvnE;': '\u2269\ufe00',
'Hacek;': '\u02c7',
'hairsp;': '\u200a',
'half;': '\xbd',
'hamilt;': '\u210b',
'HARDcy;': '\u042a',
'hardcy;': '\u044a',
'hArr;': '\u21d4',
'harr;': '\u2194',
'harrcir;': '\u2948',
'harrw;': '\u21ad',
'Hat;': '^',
'hbar;': '\u210f',
'Hcirc;': '\u0124',
'hcirc;': '\u0125',
'hearts;': '\u2665',
'heartsuit;': '\u2665',
'hellip;': '\u2026',
'hercon;': '\u22b9',
'Hfr;': '\u210c',
'hfr;': '\U0001d525',
'HilbertSpace;': '\u210b',
'hksearow;': '\u2925',
'hkswarow;': '\u2926',
'hoarr;': '\u21ff',
'homtht;': '\u223b',
'hookleftarrow;': '\u21a9',
'hookrightarrow;': '\u21aa',
'Hopf;': '\u210d',
'hopf;': '\U0001d559',
'horbar;': '\u2015',
'HorizontalLine;': '\u2500',
'Hscr;': '\u210b',
'hscr;': '\U0001d4bd',
'hslash;': '\u210f',
'Hstrok;': '\u0126',
'hstrok;': '\u0127',
'HumpDownHump;': '\u224e',
'HumpEqual;': '\u224f',
'hybull;': '\u2043',
'hyphen;': '\u2010',
'Iacute': '\xcd',
'iacute': '\xed',
'Iacute;': '\xcd',
'iacute;': '\xed',
'ic;': '\u2063',
'Icirc': '\xce',
'icirc': '\xee',
'Icirc;': '\xce',
'icirc;': '\xee',
'Icy;': '\u0418',
'icy;': '\u0438',
'Idot;': '\u0130',
'IEcy;': '\u0415',
'iecy;': '\u0435',
'iexcl': '\xa1',
'iexcl;': '\xa1',
'iff;': '\u21d4',
'Ifr;': '\u2111',
'ifr;': '\U0001d526',
'Igrave': '\xcc',
'igrave': '\xec',
'Igrave;': '\xcc',
'igrave;': '\xec',
'ii;': '\u2148',
'iiiint;': '\u2a0c',
'iiint;': '\u222d',
'iinfin;': '\u29dc',
'iiota;': '\u2129',
'IJlig;': '\u0132',
'ijlig;': '\u0133',
'Im;': '\u2111',
'Imacr;': '\u012a',
'imacr;': '\u012b',
'image;': '\u2111',
'ImaginaryI;': '\u2148',
'imagline;': '\u2110',
'imagpart;': '\u2111',
'imath;': '\u0131',
'imof;': '\u22b7',
'imped;': '\u01b5',
'Implies;': '\u21d2',
'in;': '\u2208',
'incare;': '\u2105',
'infin;': '\u221e',
'infintie;': '\u29dd',
'inodot;': '\u0131',
'Int;': '\u222c',
'int;': '\u222b',
'intcal;': '\u22ba',
'integers;': '\u2124',
'Integral;': '\u222b',
'intercal;': '\u22ba',
'Intersection;': '\u22c2',
'intlarhk;': '\u2a17',
'intprod;': '\u2a3c',
'InvisibleComma;': '\u2063',
'InvisibleTimes;': '\u2062',
'IOcy;': '\u0401',
'iocy;': '\u0451',
'Iogon;': '\u012e',
'iogon;': '\u012f',
'Iopf;': '\U0001d540',
'iopf;': '\U0001d55a',
'Iota;': '\u0399',
'iota;': '\u03b9',
'iprod;': '\u2a3c',
'iquest': '\xbf',
'iquest;': '\xbf',
'Iscr;': '\u2110',
'iscr;': '\U0001d4be',
'isin;': '\u2208',
'isindot;': '\u22f5',
'isinE;': '\u22f9',
'isins;': '\u22f4',
'isinsv;': '\u22f3',
'isinv;': '\u2208',
'it;': '\u2062',
'Itilde;': '\u0128',
'itilde;': '\u0129',
'Iukcy;': '\u0406',
'iukcy;': '\u0456',
'Iuml': '\xcf',
'iuml': '\xef',
'Iuml;': '\xcf',
'iuml;': '\xef',
'Jcirc;': '\u0134',
'jcirc;': '\u0135',
'Jcy;': '\u0419',
'jcy;': '\u0439',
'Jfr;': '\U0001d50d',
'jfr;': '\U0001d527',
'jmath;': '\u0237',
'Jopf;': '\U0001d541',
'jopf;': '\U0001d55b',
'Jscr;': '\U0001d4a5',
'jscr;': '\U0001d4bf',
'Jsercy;': '\u0408',
'jsercy;': '\u0458',
'Jukcy;': '\u0404',
'jukcy;': '\u0454',
'Kappa;': '\u039a',
'kappa;': '\u03ba',
'kappav;': '\u03f0',
'Kcedil;': '\u0136',
'kcedil;': '\u0137',
'Kcy;': '\u041a',
'kcy;': '\u043a',
'Kfr;': '\U0001d50e',
'kfr;': '\U0001d528',
'kgreen;': '\u0138',
'KHcy;': '\u0425',
'khcy;': '\u0445',
'KJcy;': '\u040c',
'kjcy;': '\u045c',
'Kopf;': '\U0001d542',
'kopf;': '\U0001d55c',
'Kscr;': '\U0001d4a6',
'kscr;': '\U0001d4c0',
'lAarr;': '\u21da',
'Lacute;': '\u0139',
'lacute;': '\u013a',
'laemptyv;': '\u29b4',
'lagran;': '\u2112',
'Lambda;': '\u039b',
'lambda;': '\u03bb',
'Lang;': '\u27ea',
'lang;': '\u27e8',
'langd;': '\u2991',
'langle;': '\u27e8',
'lap;': '\u2a85',
'Laplacetrf;': '\u2112',
'laquo': '\xab',
'laquo;': '\xab',
'Larr;': '\u219e',
'lArr;': '\u21d0',
'larr;': '\u2190',
'larrb;': '\u21e4',
'larrbfs;': '\u291f',
'larrfs;': '\u291d',
'larrhk;': '\u21a9',
'larrlp;': '\u21ab',
'larrpl;': '\u2939',
'larrsim;': '\u2973',
'larrtl;': '\u21a2',
'lat;': '\u2aab',
'lAtail;': '\u291b',
'latail;': '\u2919',
'late;': '\u2aad',
'lates;': '\u2aad\ufe00',
'lBarr;': '\u290e',
'lbarr;': '\u290c',
'lbbrk;': '\u2772',
'lbrace;': '{',
'lbrack;': '[',
'lbrke;': '\u298b',
'lbrksld;': '\u298f',
'lbrkslu;': '\u298d',
'Lcaron;': '\u013d',
'lcaron;': '\u013e',
'Lcedil;': '\u013b',
'lcedil;': '\u013c',
'lceil;': '\u2308',
'lcub;': '{',
'Lcy;': '\u041b',
'lcy;': '\u043b',
'ldca;': '\u2936',
'ldquo;': '\u201c',
'ldquor;': '\u201e',
'ldrdhar;': '\u2967',
'ldrushar;': '\u294b',
'ldsh;': '\u21b2',
'lE;': '\u2266',
'le;': '\u2264',
'LeftAngleBracket;': '\u27e8',
'LeftArrow;': '\u2190',
'Leftarrow;': '\u21d0',
'leftarrow;': '\u2190',
'LeftArrowBar;': '\u21e4',
'LeftArrowRightArrow;': '\u21c6',
'leftarrowtail;': '\u21a2',
'LeftCeiling;': '\u2308',
'LeftDoubleBracket;': '\u27e6',
'LeftDownTeeVector;': '\u2961',
'LeftDownVector;': '\u21c3',
'LeftDownVectorBar;': '\u2959',
'LeftFloor;': '\u230a',
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | true |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/version.py | youtube_dl/version.py | from __future__ import unicode_literals
__version__ = '2025.04.07'
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/YoutubeDL.py | youtube_dl/YoutubeDL.py | #!/usr/bin/env python
# coding: utf-8
from __future__ import absolute_import, unicode_literals
import collections
import copy
import datetime
import errno
import functools
import io
import itertools
import json
import locale
import operator
import os
import platform
import re
import shutil
import subprocess
import socket
import sys
import time
import tokenize
import traceback
import random
try:
from ssl import OPENSSL_VERSION
except ImportError:
# Must be Python 2.6, should be built against 1.0.2
OPENSSL_VERSION = 'OpenSSL 1.0.2(?)'
from string import ascii_letters
from .compat import (
compat_basestring,
compat_collections_chain_map as ChainMap,
compat_filter as filter,
compat_get_terminal_size,
compat_http_client,
compat_http_cookiejar_Cookie,
compat_http_cookies_SimpleCookie,
compat_integer_types,
compat_kwargs,
compat_map as map,
compat_numeric_types,
compat_open as open,
compat_os_name,
compat_str,
compat_tokenize_tokenize,
compat_urllib_error,
compat_urllib_parse,
compat_urllib_request,
compat_urllib_request_DataHandler,
)
from .utils import (
_UnsafeExtensionError,
age_restricted,
args_to_str,
bug_reports_message,
ContentTooShortError,
date_from_str,
DateRange,
DEFAULT_OUTTMPL,
determine_ext,
determine_protocol,
DownloadError,
encode_compat_str,
encodeFilename,
error_to_compat_str,
expand_path,
ExtractorError,
format_bytes,
formatSeconds,
GeoRestrictedError,
int_or_none,
ISO3166Utils,
join_nonempty,
locked_file,
LazyList,
make_HTTPS_handler,
MaxDownloadsReached,
orderedSet,
PagedList,
parse_filesize,
PerRequestProxyHandler,
platform_name,
PostProcessingError,
preferredencoding,
prepend_extension,
process_communicate_or_kill,
register_socks_protocols,
render_table,
replace_extension,
SameFileError,
sanitize_filename,
sanitize_path,
sanitize_url,
sanitized_Request,
std_headers,
str_or_none,
subtitles_filename,
traverse_obj,
UnavailableVideoError,
url_basename,
version_tuple,
write_json_file,
write_string,
YoutubeDLCookieJar,
YoutubeDLCookieProcessor,
YoutubeDLHandler,
YoutubeDLRedirectHandler,
ytdl_is_updateable,
)
from .cache import Cache
from .extractor import get_info_extractor, gen_extractor_classes, _LAZY_LOADER
from .extractor.openload import PhantomJSwrapper
from .downloader import get_suitable_downloader
from .downloader.rtmp import rtmpdump_version
from .postprocessor import (
FFmpegFixupM3u8PP,
FFmpegFixupM4aPP,
FFmpegFixupStretchedPP,
FFmpegMergerPP,
FFmpegPostProcessor,
get_postprocessor,
)
from .version import __version__
if compat_os_name == 'nt':
import ctypes
def _catch_unsafe_file_extension(func):
@functools.wraps(func)
def wrapper(self, *args, **kwargs):
try:
return func(self, *args, **kwargs)
except _UnsafeExtensionError as error:
self.report_error(
'{0} found; to avoid damaging your system, this value is disallowed.'
' If you believe this is an error{1}'.format(
error_to_compat_str(error), bug_reports_message(',')))
return wrapper
class YoutubeDL(object):
"""YoutubeDL class.
YoutubeDL objects are the ones responsible of downloading the
actual video file and writing it to disk if the user has requested
it, among some other tasks. In most cases there should be one per
program. As, given a video URL, the downloader doesn't know how to
extract all the needed information, task that InfoExtractors do, it
has to pass the URL to one of them.
For this, YoutubeDL objects have a method that allows
InfoExtractors to be registered in a given order. When it is passed
a URL, the YoutubeDL object handles it to the first InfoExtractor it
finds that reports being able to handle it. The InfoExtractor extracts
all the information about the video or videos the URL refers to, and
YoutubeDL process the extracted information, possibly using a File
Downloader to download the video.
YoutubeDL objects accept a lot of parameters. In order not to saturate
the object constructor with arguments, it receives a dictionary of
options instead. These options are available through the params
attribute for the InfoExtractors to use. The YoutubeDL also
registers itself as the downloader in charge for the InfoExtractors
that are added to it, so this is a "mutual registration".
Available options:
username: Username for authentication purposes.
password: Password for authentication purposes.
videopassword: Password for accessing a video.
ap_mso: Adobe Pass multiple-system operator identifier.
ap_username: Multiple-system operator account username.
ap_password: Multiple-system operator account password.
usenetrc: Use netrc for authentication instead.
verbose: Print additional info to stdout.
quiet: Do not print messages to stdout.
no_warnings: Do not print out anything for warnings.
forceurl: Force printing final URL.
forcetitle: Force printing title.
forceid: Force printing ID.
forcethumbnail: Force printing thumbnail URL.
forcedescription: Force printing description.
forcefilename: Force printing final filename.
forceduration: Force printing duration.
forcejson: Force printing info_dict as JSON.
dump_single_json: Force printing the info_dict of the whole playlist
(or video) as a single JSON line.
simulate: Do not download the video files.
format: Video format code. See options.py for more information.
outtmpl: Template for output names.
outtmpl_na_placeholder: Placeholder for unavailable meta fields.
restrictfilenames: Do not allow "&" and spaces in file names
ignoreerrors: Do not stop on download errors.
force_generic_extractor: Force downloader to use the generic extractor
nooverwrites: Prevent overwriting files.
playliststart: Playlist item to start at.
playlistend: Playlist item to end at.
playlist_items: Specific indices of playlist to download.
playlistreverse: Download playlist items in reverse order.
playlistrandom: Download playlist items in random order.
matchtitle: Download only matching titles.
rejecttitle: Reject downloads for matching titles.
logger: Log messages to a logging.Logger instance.
logtostderr: Log messages to stderr instead of stdout.
writedescription: Write the video description to a .description file
writeinfojson: Write the video description to a .info.json file
writeannotations: Write the video annotations to a .annotations.xml file
writethumbnail: Write the thumbnail image to a file
write_all_thumbnails: Write all thumbnail formats to files
writesubtitles: Write the video subtitles to a file
writeautomaticsub: Write the automatically generated subtitles to a file
allsubtitles: Downloads all the subtitles of the video
(requires writesubtitles or writeautomaticsub)
listsubtitles: Lists all available subtitles for the video
subtitlesformat: The format code for subtitles
subtitleslangs: List of languages of the subtitles to download
keepvideo: Keep the video file after post-processing
daterange: A DateRange object, download only if the upload_date is in the range.
skip_download: Skip the actual download of the video file
cachedir: Location of the cache files in the filesystem.
False to disable filesystem cache.
noplaylist: Download single video instead of a playlist if in doubt.
age_limit: An integer representing the user's age in years.
Unsuitable videos for the given age are skipped.
min_views: An integer representing the minimum view count the video
must have in order to not be skipped.
Videos without view count information are always
downloaded. None for no limit.
max_views: An integer representing the maximum view count.
Videos that are more popular than that are not
downloaded.
Videos without view count information are always
downloaded. None for no limit.
download_archive: File name of a file where all downloads are recorded.
Videos already present in the file are not downloaded
again.
cookiefile: File name where cookies should be read from and dumped to.
nocheckcertificate:Do not verify SSL certificates
prefer_insecure: Use HTTP instead of HTTPS to retrieve information.
At the moment, this is only supported by YouTube.
proxy: URL of the proxy server to use
geo_verification_proxy: URL of the proxy to use for IP address verification
on geo-restricted sites.
socket_timeout: Time to wait for unresponsive hosts, in seconds
bidi_workaround: Work around buggy terminals without bidirectional text
support, using fridibi
debug_printtraffic:Print out sent and received HTTP traffic
include_ads: Download ads as well
default_search: Prepend this string if an input url is not valid.
'auto' for elaborate guessing
encoding: Use this encoding instead of the system-specified.
extract_flat: Do not resolve URLs, return the immediate result.
Pass in 'in_playlist' to only show this behavior for
playlist items.
postprocessors: A list of dictionaries, each with an entry
* key: The name of the postprocessor. See
youtube_dl/postprocessor/__init__.py for a list.
as well as any further keyword arguments for the
postprocessor.
progress_hooks: A list of functions that get called on download
progress, with a dictionary with the entries
* status: One of "downloading", "error", or "finished".
Check this first and ignore unknown values.
If status is one of "downloading", or "finished", the
following properties may also be present:
* filename: The final filename (always present)
* tmpfilename: The filename we're currently writing to
* downloaded_bytes: Bytes on disk
* total_bytes: Size of the whole file, None if unknown
* total_bytes_estimate: Guess of the eventual file size,
None if unavailable.
* elapsed: The number of seconds since download started.
* eta: The estimated time in seconds, None if unknown
* speed: The download speed in bytes/second, None if
unknown
* fragment_index: The counter of the currently
downloaded video fragment.
* fragment_count: The number of fragments (= individual
files that will be merged)
Progress hooks are guaranteed to be called at least once
(with status "finished") if the download is successful.
merge_output_format: Extension to use when merging formats.
fixup: Automatically correct known faults of the file.
One of:
- "never": do nothing
- "warn": only emit a warning
- "detect_or_warn": check whether we can do anything
about it, warn otherwise (default)
source_address: Client-side IP address to bind to.
call_home: Boolean, true iff we are allowed to contact the
youtube-dl servers for debugging.
sleep_interval: Number of seconds to sleep before each download when
used alone or a lower bound of a range for randomized
sleep before each download (minimum possible number
of seconds to sleep) when used along with
max_sleep_interval.
max_sleep_interval:Upper bound of a range for randomized sleep before each
download (maximum possible number of seconds to sleep).
Must only be used along with sleep_interval.
Actual sleep time will be a random float from range
[sleep_interval; max_sleep_interval].
listformats: Print an overview of available video formats and exit.
list_thumbnails: Print a table of all thumbnails and exit.
match_filter: A function that gets called with the info_dict of
every video.
If it returns a message, the video is ignored.
If it returns None, the video is downloaded.
match_filter_func in utils.py is one example for this.
no_color: Do not emit color codes in output.
geo_bypass: Bypass geographic restriction via faking X-Forwarded-For
HTTP header
geo_bypass_country:
Two-letter ISO 3166-2 country code that will be used for
explicit geographic restriction bypassing via faking
X-Forwarded-For HTTP header
geo_bypass_ip_block:
IP range in CIDR notation that will be used similarly to
geo_bypass_country
The following options determine which downloader is picked:
external_downloader: Executable of the external downloader to call.
None or unset for standard (built-in) downloader.
hls_prefer_native: Use the native HLS downloader instead of ffmpeg/avconv
if True, otherwise use ffmpeg/avconv if False, otherwise
use downloader suggested by extractor if None.
The following parameters are not used by YoutubeDL itself, they are used by
the downloader (see youtube_dl/downloader/common.py):
nopart, updatetime, buffersize, ratelimit, min_filesize, max_filesize, test,
noresizebuffer, retries, continuedl, noprogress, consoletitle,
xattr_set_filesize, external_downloader_args, hls_use_mpegts,
http_chunk_size.
The following options are used by the post processors:
prefer_ffmpeg: If False, use avconv instead of ffmpeg if both are available,
otherwise prefer ffmpeg.
ffmpeg_location: Location of the ffmpeg/avconv binary; either the path
to the binary or its containing directory.
postprocessor_args: A list of additional command-line arguments for the
postprocessor.
The following options are used by the Youtube extractor:
youtube_include_dash_manifest: If True (default), DASH manifests and related
data will be downloaded and processed by extractor.
You can reduce network I/O by disabling it if you don't
care about DASH.
"""
_NUMERIC_FIELDS = set((
'width', 'height', 'tbr', 'abr', 'asr', 'vbr', 'fps', 'filesize', 'filesize_approx',
'timestamp', 'upload_year', 'upload_month', 'upload_day', 'available_at',
'duration', 'view_count', 'like_count', 'dislike_count', 'repost_count',
'average_rating', 'comment_count', 'age_limit',
'start_time', 'end_time',
'chapter_number', 'season_number', 'episode_number',
'track_number', 'disc_number', 'release_year',
'playlist_index',
))
params = None
_ies = []
_pps = []
_download_retcode = None
_num_downloads = None
_playlist_level = 0
_playlist_urls = set()
_screen_file = None
def __init__(self, params=None, auto_init=True):
"""Create a FileDownloader object with the given options."""
if params is None:
params = {}
self._ies = []
self._ies_instances = {}
self._pps = []
self._progress_hooks = []
self._download_retcode = 0
self._num_downloads = 0
self._screen_file = [sys.stdout, sys.stderr][params.get('logtostderr', False)]
self._err_file = sys.stderr
self.params = {
# Default parameters
'nocheckcertificate': False,
}
self.params.update(params)
self.cache = Cache(self)
self._header_cookies = []
self._load_cookies_from_headers(self.params.get('http_headers'))
def check_deprecated(param, option, suggestion):
if self.params.get(param) is not None:
self.report_warning(
'%s is deprecated. Use %s instead.' % (option, suggestion))
return True
return False
if check_deprecated('cn_verification_proxy', '--cn-verification-proxy', '--geo-verification-proxy'):
if self.params.get('geo_verification_proxy') is None:
self.params['geo_verification_proxy'] = self.params['cn_verification_proxy']
check_deprecated('autonumber_size', '--autonumber-size', 'output template with %(autonumber)0Nd, where N in the number of digits')
check_deprecated('autonumber', '--auto-number', '-o "%(autonumber)s-%(title)s.%(ext)s"')
check_deprecated('usetitle', '--title', '-o "%(title)s-%(id)s.%(ext)s"')
if params.get('bidi_workaround', False):
try:
import pty
master, slave = pty.openpty()
width = compat_get_terminal_size().columns
if width is None:
width_args = []
else:
width_args = ['-w', str(width)]
sp_kwargs = dict(
stdin=subprocess.PIPE,
stdout=slave,
stderr=self._err_file)
try:
self._output_process = subprocess.Popen(
['bidiv'] + width_args, **sp_kwargs
)
except OSError:
self._output_process = subprocess.Popen(
['fribidi', '-c', 'UTF-8'] + width_args, **sp_kwargs)
self._output_channel = os.fdopen(master, 'rb')
except OSError as ose:
if ose.errno == errno.ENOENT:
self.report_warning('Could not find fribidi executable, ignoring --bidi-workaround . Make sure that fribidi is an executable file in one of the directories in your $PATH.')
else:
raise
if (sys.platform != 'win32'
and sys.getfilesystemencoding() in ['ascii', 'ANSI_X3.4-1968']
and not params.get('restrictfilenames', False)):
# Unicode filesystem API will throw errors (#1474, #13027)
self.report_warning(
'Assuming --restrict-filenames since file system encoding '
'cannot encode all characters. '
'Set the LC_ALL environment variable to fix this.')
self.params['restrictfilenames'] = True
if isinstance(params.get('outtmpl'), bytes):
self.report_warning(
'Parameter outtmpl is bytes, but should be a unicode string. '
'Put from __future__ import unicode_literals at the top of your code file or consider switching to Python 3.x.')
self._setup_opener()
if auto_init:
self.print_debug_header()
self.add_default_info_extractors()
for pp_def_raw in self.params.get('postprocessors', []):
pp_class = get_postprocessor(pp_def_raw['key'])
pp_def = dict(pp_def_raw)
del pp_def['key']
pp = pp_class(self, **compat_kwargs(pp_def))
self.add_post_processor(pp)
for ph in self.params.get('progress_hooks', []):
self.add_progress_hook(ph)
register_socks_protocols()
def warn_if_short_id(self, argv):
# short YouTube ID starting with dash?
idxs = [
i for i, a in enumerate(argv)
if re.match(r'^-[0-9A-Za-z_-]{10}$', a)]
if idxs:
correct_argv = (
['youtube-dl']
+ [a for i, a in enumerate(argv) if i not in idxs]
+ ['--'] + [argv[i] for i in idxs]
)
self.report_warning(
'Long argument string detected. '
'Use -- to separate parameters and URLs, like this:\n%s\n' %
args_to_str(correct_argv))
def add_info_extractor(self, ie):
"""Add an InfoExtractor object to the end of the list."""
self._ies.append(ie)
if not isinstance(ie, type):
self._ies_instances[ie.ie_key()] = ie
ie.set_downloader(self)
def get_info_extractor(self, ie_key):
"""
Get an instance of an IE with name ie_key, it will try to get one from
the _ies list, if there's no instance it will create a new one and add
it to the extractor list.
"""
ie = self._ies_instances.get(ie_key)
if ie is None:
ie = get_info_extractor(ie_key)()
self.add_info_extractor(ie)
return ie
def add_default_info_extractors(self):
"""
Add the InfoExtractors returned by gen_extractors to the end of the list
"""
for ie in gen_extractor_classes():
self.add_info_extractor(ie)
def add_post_processor(self, pp):
"""Add a PostProcessor object to the end of the chain."""
self._pps.append(pp)
pp.set_downloader(self)
def add_progress_hook(self, ph):
"""Add the progress hook (currently only for the file downloader)"""
self._progress_hooks.append(ph)
def _bidi_workaround(self, message):
if not hasattr(self, '_output_channel'):
return message
assert hasattr(self, '_output_process')
assert isinstance(message, compat_str)
line_count = message.count('\n') + 1
self._output_process.stdin.write((message + '\n').encode('utf-8'))
self._output_process.stdin.flush()
res = ''.join(self._output_channel.readline().decode('utf-8')
for _ in range(line_count))
return res[:-len('\n')]
def to_screen(self, message, skip_eol=False):
"""Print message to stdout if not in quiet mode."""
return self.to_stdout(message, skip_eol, check_quiet=True)
def _write_string(self, s, out=None, only_once=False, _cache=set()):
if only_once and s in _cache:
return
write_string(s, out=out, encoding=self.params.get('encoding'))
if only_once:
_cache.add(s)
def to_stdout(self, message, skip_eol=False, check_quiet=False, only_once=False):
"""Print message to stdout if not in quiet mode."""
if self.params.get('logger'):
self.params['logger'].debug(message)
elif not check_quiet or not self.params.get('quiet', False):
message = self._bidi_workaround(message)
terminator = ['\n', ''][skip_eol]
output = message + terminator
self._write_string(output, self._screen_file, only_once=only_once)
def to_stderr(self, message, only_once=False):
"""Print message to stderr."""
assert isinstance(message, compat_str)
if self.params.get('logger'):
self.params['logger'].error(message)
else:
message = self._bidi_workaround(message)
output = message + '\n'
self._write_string(output, self._err_file, only_once=only_once)
def to_console_title(self, message):
if not self.params.get('consoletitle', False):
return
if compat_os_name == 'nt':
if ctypes.windll.kernel32.GetConsoleWindow():
# c_wchar_p() might not be necessary if `message` is
# already of type unicode()
ctypes.windll.kernel32.SetConsoleTitleW(ctypes.c_wchar_p(message))
elif 'TERM' in os.environ:
self._write_string('\033]0;%s\007' % message, self._screen_file)
def save_console_title(self):
if not self.params.get('consoletitle', False):
return
if self.params.get('simulate', False):
return
if compat_os_name != 'nt' and 'TERM' in os.environ:
# Save the title on stack
self._write_string('\033[22;0t', self._screen_file)
def restore_console_title(self):
if not self.params.get('consoletitle', False):
return
if self.params.get('simulate', False):
return
if compat_os_name != 'nt' and 'TERM' in os.environ:
# Restore the title from stack
self._write_string('\033[23;0t', self._screen_file)
def __enter__(self):
self.save_console_title()
return self
def __exit__(self, *args):
self.restore_console_title()
if self.params.get('cookiefile') is not None:
self.cookiejar.save(ignore_discard=True, ignore_expires=True)
def trouble(self, *args, **kwargs):
"""Determine action to take when a download problem appears.
Depending on if the downloader has been configured to ignore
download errors or not, this method may throw an exception or
not when errors are found, after printing the message.
tb, if given, is additional traceback information.
"""
# message=None, tb=None, is_error=True
message = args[0] if len(args) > 0 else kwargs.get('message', None)
tb = args[1] if len(args) > 1 else kwargs.get('tb', None)
is_error = args[2] if len(args) > 2 else kwargs.get('is_error', True)
if message is not None:
self.to_stderr(message)
if self.params.get('verbose'):
if tb is None:
if sys.exc_info()[0]: # if .trouble has been called from an except block
tb = ''
if hasattr(sys.exc_info()[1], 'exc_info') and sys.exc_info()[1].exc_info[0]:
tb += ''.join(traceback.format_exception(*sys.exc_info()[1].exc_info))
tb += encode_compat_str(traceback.format_exc())
else:
tb_data = traceback.format_list(traceback.extract_stack())
tb = ''.join(tb_data)
if tb:
self.to_stderr(tb)
if not is_error:
return
if not self.params.get('ignoreerrors', False):
if sys.exc_info()[0] and hasattr(sys.exc_info()[1], 'exc_info') and sys.exc_info()[1].exc_info[0]:
exc_info = sys.exc_info()[1].exc_info
else:
exc_info = sys.exc_info()
raise DownloadError(message, exc_info)
self._download_retcode = 1
def report_warning(self, message, only_once=False):
'''
Print the message to stderr, it will be prefixed with 'WARNING:'
If stderr is a tty file the 'WARNING:' will be colored
'''
if self.params.get('logger') is not None:
self.params['logger'].warning(message)
else:
if self.params.get('no_warnings'):
return
if not self.params.get('no_color') and self._err_file.isatty() and compat_os_name != 'nt':
_msg_header = '\033[0;33mWARNING:\033[0m'
else:
_msg_header = 'WARNING:'
warning_message = '%s %s' % (_msg_header, message)
self.to_stderr(warning_message, only_once=only_once)
def report_error(self, message, *args, **kwargs):
'''
Do the same as trouble, but prefixes the message with 'ERROR:', colored
in red if stderr is a tty file.
'''
if not self.params.get('no_color') and self._err_file.isatty() and compat_os_name != 'nt':
_msg_header = '\033[0;31mERROR:\033[0m'
else:
_msg_header = 'ERROR:'
kwargs['message'] = '%s %s' % (_msg_header, message)
self.trouble(*args, **kwargs)
def write_debug(self, message, only_once=False):
'''Log debug message or Print message to stderr'''
if not self.params.get('verbose', False):
return
message = '[debug] {0}'.format(message)
if self.params.get('logger'):
self.params['logger'].debug(message)
else:
self.to_stderr(message, only_once)
def report_unscoped_cookies(self, *args, **kwargs):
# message=None, tb=False, is_error=False
if len(args) <= 2:
kwargs.setdefault('is_error', False)
if len(args) <= 0:
kwargs.setdefault(
'message',
'Unscoped cookies are not allowed: please specify some sort of scoping')
self.report_error(*args, **kwargs)
def report_file_already_downloaded(self, file_name):
"""Report file has already been fully downloaded."""
try:
self.to_screen('[download] %s has already been downloaded' % file_name)
except UnicodeEncodeError:
self.to_screen('[download] The file has already been downloaded')
def prepare_filename(self, info_dict):
"""Generate the output filename."""
try:
template_dict = dict(info_dict)
template_dict['epoch'] = int(time.time())
autonumber_size = self.params.get('autonumber_size')
if autonumber_size is None:
autonumber_size = 5
template_dict['autonumber'] = self.params.get('autonumber_start', 1) - 1 + self._num_downloads
if template_dict.get('resolution') is None:
if template_dict.get('width') and template_dict.get('height'):
template_dict['resolution'] = '%dx%d' % (template_dict['width'], template_dict['height'])
elif template_dict.get('height'):
template_dict['resolution'] = '%sp' % template_dict['height']
elif template_dict.get('width'):
template_dict['resolution'] = '%dx?' % template_dict['width']
sanitize = lambda k, v: sanitize_filename(
compat_str(v),
restricted=self.params.get('restrictfilenames'),
is_id=(k == 'id' or k.endswith('_id')))
template_dict = dict((k, v if isinstance(v, compat_numeric_types) else sanitize(k, v))
for k, v in template_dict.items()
if v is not None and not isinstance(v, (list, tuple, dict)))
template_dict = collections.defaultdict(lambda: self.params.get('outtmpl_na_placeholder', 'NA'), template_dict)
outtmpl = self.params.get('outtmpl', DEFAULT_OUTTMPL)
# For fields playlist_index and autonumber convert all occurrences
# of %(field)s to %(field)0Nd for backward compatibility
field_size_compat_map = {
'playlist_index': len(str(template_dict['n_entries'])),
'autonumber': autonumber_size,
}
FIELD_SIZE_COMPAT_RE = r'(?<!%)%\((?P<field>autonumber|playlist_index)\)s'
mobj = re.search(FIELD_SIZE_COMPAT_RE, outtmpl)
if mobj:
outtmpl = re.sub(
FIELD_SIZE_COMPAT_RE,
r'%%(\1)0%dd' % field_size_compat_map[mobj.group('field')],
outtmpl)
# Missing numeric fields used together with integer presentation types
# in format specification will break the argument substitution since
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | true |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/__main__.py | youtube_dl/__main__.py | #!/usr/bin/env python
from __future__ import unicode_literals
# Execute with
# $ python youtube_dl/__main__.py (2.6+)
# $ python -m youtube_dl (2.7+)
import sys
if __package__ is None and not hasattr(sys, 'frozen'):
# direct call of __main__.py
import os.path
path = os.path.realpath(os.path.abspath(__file__))
sys.path.insert(0, os.path.dirname(os.path.dirname(path)))
import youtube_dl
if __name__ == '__main__':
youtube_dl.main()
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/update.py | youtube_dl/update.py | from __future__ import unicode_literals
import json
import traceback
import hashlib
import os
import subprocess
import sys
from zipimport import zipimporter
from .compat import (
compat_open as open,
compat_realpath,
)
from .utils import encode_compat_str
from .version import __version__
def rsa_verify(message, signature, key):
from hashlib import sha256
assert isinstance(message, bytes)
byte_size = (len(bin(key[0])) - 2 + 8 - 1) // 8
signature = ('%x' % pow(int(signature, 16), key[1], key[0])).encode()
signature = (byte_size * 2 - len(signature)) * b'0' + signature
asn1 = b'3031300d060960864801650304020105000420'
asn1 += sha256(message).hexdigest().encode()
if byte_size < len(asn1) // 2 + 11:
return False
expected = b'0001' + (byte_size - len(asn1) // 2 - 3) * b'ff' + b'00' + asn1
return expected == signature
def update_self(to_screen, verbose, opener):
"""Update the program file with the latest version from the repository"""
UPDATE_URL = 'https://yt-dl.org/update/'
VERSION_URL = UPDATE_URL + 'LATEST_VERSION'
JSON_URL = UPDATE_URL + 'versions.json'
UPDATES_RSA_KEY = (0x9d60ee4d8f805312fdb15a62f87b95bd66177b91df176765d13514a0f1754bcd2057295c5b6f1d35daa6742c3ffc9a82d3e118861c207995a8031e151d863c9927e304576bc80692bc8e094896fcf11b66f3e29e04e3a71e9a11558558acea1840aec37fc396fb6b65dc81a1c4144e03bd1c011de62e3f1357b327d08426fe93, 65537)
if not isinstance(globals().get('__loader__'), zipimporter) and not hasattr(sys, 'frozen'):
to_screen('It looks like you installed youtube-dl with a package manager, pip, setup.py or a tarball. Please use that to update.')
return
# Check if there is a new version
try:
newversion = opener.open(VERSION_URL).read().decode('utf-8').strip()
except Exception:
if verbose:
to_screen(encode_compat_str(traceback.format_exc()))
to_screen('ERROR: can\'t find the current version. Please try again later.')
return
if newversion == __version__:
to_screen('youtube-dl is up-to-date (' + __version__ + ')')
return
# Download and check versions info
try:
versions_info = opener.open(JSON_URL).read().decode('utf-8')
versions_info = json.loads(versions_info)
except Exception:
if verbose:
to_screen(encode_compat_str(traceback.format_exc()))
to_screen('ERROR: can\'t obtain versions info. Please try again later.')
return
if 'signature' not in versions_info:
to_screen('ERROR: the versions file is not signed or corrupted. Aborting.')
return
signature = versions_info['signature']
del versions_info['signature']
if not rsa_verify(json.dumps(versions_info, sort_keys=True).encode('utf-8'), signature, UPDATES_RSA_KEY):
to_screen('ERROR: the versions file signature is invalid. Aborting.')
return
version_id = versions_info['latest']
def version_tuple(version_str):
return tuple(map(int, version_str.split('.')))
if version_tuple(__version__) >= version_tuple(version_id):
to_screen('youtube-dl is up to date (%s)' % __version__)
return
to_screen('Updating to version ' + version_id + ' ...')
version = versions_info['versions'][version_id]
print_notes(to_screen, versions_info['versions'])
# sys.executable is set to the full pathname of the exe-file for py2exe
# though symlinks are not followed so that we need to do this manually
# with help of realpath
filename = compat_realpath(sys.executable if hasattr(sys, 'frozen') else sys.argv[0])
if not os.access(filename, os.W_OK):
to_screen('ERROR: no write permissions on %s' % filename)
return
# Py2EXE
if hasattr(sys, 'frozen'):
exe = filename
directory = os.path.dirname(exe)
if not os.access(directory, os.W_OK):
to_screen('ERROR: no write permissions on %s' % directory)
return
try:
urlh = opener.open(version['exe'][0])
newcontent = urlh.read()
urlh.close()
except (IOError, OSError):
if verbose:
to_screen(encode_compat_str(traceback.format_exc()))
to_screen('ERROR: unable to download latest version')
return
newcontent_hash = hashlib.sha256(newcontent).hexdigest()
if newcontent_hash != version['exe'][1]:
to_screen('ERROR: the downloaded file hash does not match. Aborting.')
return
try:
with open(exe + '.new', 'wb') as outf:
outf.write(newcontent)
except (IOError, OSError):
if verbose:
to_screen(encode_compat_str(traceback.format_exc()))
to_screen('ERROR: unable to write the new version')
return
try:
bat = os.path.join(directory, 'youtube-dl-updater.bat')
with open(bat, 'w') as batfile:
batfile.write('''
@echo off
echo Waiting for file handle to be closed ...
ping 127.0.0.1 -n 5 -w 1000 > NUL
move /Y "%s.new" "%s" > NUL
echo Updated youtube-dl to version %s.
start /b "" cmd /c del "%%~f0"&exit /b"
\n''' % (exe, exe, version_id))
subprocess.Popen([bat]) # Continues to run in the background
return # Do not show premature success messages
except (IOError, OSError):
if verbose:
to_screen(encode_compat_str(traceback.format_exc()))
to_screen('ERROR: unable to overwrite current version')
return
# Zip unix package
elif isinstance(globals().get('__loader__'), zipimporter):
try:
urlh = opener.open(version['bin'][0])
newcontent = urlh.read()
urlh.close()
except (IOError, OSError):
if verbose:
to_screen(encode_compat_str(traceback.format_exc()))
to_screen('ERROR: unable to download latest version')
return
newcontent_hash = hashlib.sha256(newcontent).hexdigest()
if newcontent_hash != version['bin'][1]:
to_screen('ERROR: the downloaded file hash does not match. Aborting.')
return
try:
with open(filename, 'wb') as outf:
outf.write(newcontent)
except (IOError, OSError):
if verbose:
to_screen(encode_compat_str(traceback.format_exc()))
to_screen('ERROR: unable to overwrite current version')
return
to_screen('Updated youtube-dl. Restart youtube-dl to use the new version.')
def get_notes(versions, fromVersion):
notes = []
for v, vdata in sorted(versions.items()):
if v > fromVersion:
notes.extend(vdata.get('notes', []))
return notes
def print_notes(to_screen, versions, fromVersion=__version__):
notes = get_notes(versions, fromVersion)
if notes:
to_screen('PLEASE NOTE:')
for note in notes:
to_screen(note)
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/socks.py | youtube_dl/socks.py | # Public Domain SOCKS proxy protocol implementation
# Adapted from https://gist.github.com/bluec0re/cafd3764412967417fd3
from __future__ import unicode_literals
# References:
# SOCKS4 protocol http://www.openssh.com/txt/socks4.protocol
# SOCKS4A protocol http://www.openssh.com/txt/socks4a.protocol
# SOCKS5 protocol https://tools.ietf.org/html/rfc1928
# SOCKS5 username/password authentication https://tools.ietf.org/html/rfc1929
import collections
import socket
from .compat import (
compat_ord,
compat_struct_pack,
compat_struct_unpack,
)
__author__ = 'Timo Schmid <coding@timoschmid.de>'
SOCKS4_VERSION = 4
SOCKS4_REPLY_VERSION = 0x00
# Excerpt from SOCKS4A protocol:
# if the client cannot resolve the destination host's domain name to find its
# IP address, it should set the first three bytes of DSTIP to NULL and the last
# byte to a non-zero value.
SOCKS4_DEFAULT_DSTIP = compat_struct_pack('!BBBB', 0, 0, 0, 0xFF)
SOCKS5_VERSION = 5
SOCKS5_USER_AUTH_VERSION = 0x01
SOCKS5_USER_AUTH_SUCCESS = 0x00
class Socks4Command(object):
CMD_CONNECT = 0x01
CMD_BIND = 0x02
class Socks5Command(Socks4Command):
CMD_UDP_ASSOCIATE = 0x03
class Socks5Auth(object):
AUTH_NONE = 0x00
AUTH_GSSAPI = 0x01
AUTH_USER_PASS = 0x02
AUTH_NO_ACCEPTABLE = 0xFF # For server response
class Socks5AddressType(object):
ATYP_IPV4 = 0x01
ATYP_DOMAINNAME = 0x03
ATYP_IPV6 = 0x04
class ProxyError(socket.error):
ERR_SUCCESS = 0x00
def __init__(self, code=None, msg=None):
if code is not None and msg is None:
msg = self.CODES.get(code) or 'unknown error'
super(ProxyError, self).__init__(code, msg)
class InvalidVersionError(ProxyError):
def __init__(self, expected_version, got_version):
msg = ('Invalid response version from server. Expected {0:02x} got '
'{1:02x}'.format(expected_version, got_version))
super(InvalidVersionError, self).__init__(0, msg)
class Socks4Error(ProxyError):
ERR_SUCCESS = 90
CODES = {
91: 'request rejected or failed',
92: 'request rejected because SOCKS server cannot connect to identd on the client',
93: 'request rejected because the client program and identd report different user-ids'
}
class Socks5Error(ProxyError):
ERR_GENERAL_FAILURE = 0x01
CODES = {
0x01: 'general SOCKS server failure',
0x02: 'connection not allowed by ruleset',
0x03: 'Network unreachable',
0x04: 'Host unreachable',
0x05: 'Connection refused',
0x06: 'TTL expired',
0x07: 'Command not supported',
0x08: 'Address type not supported',
0xFE: 'unknown username or invalid password',
0xFF: 'all offered authentication methods were rejected'
}
class ProxyType(object):
SOCKS4 = 0
SOCKS4A = 1
SOCKS5 = 2
Proxy = collections.namedtuple('Proxy', (
'type', 'host', 'port', 'username', 'password', 'remote_dns'))
class sockssocket(socket.socket):
def __init__(self, *args, **kwargs):
self._proxy = None
super(sockssocket, self).__init__(*args, **kwargs)
def setproxy(self, proxytype, addr, port, rdns=True, username=None, password=None):
assert proxytype in (ProxyType.SOCKS4, ProxyType.SOCKS4A, ProxyType.SOCKS5)
self._proxy = Proxy(proxytype, addr, port, username, password, rdns)
def recvall(self, cnt):
data = b''
while len(data) < cnt:
cur = self.recv(cnt - len(data))
if not cur:
raise EOFError('{0} bytes missing'.format(cnt - len(data)))
data += cur
return data
def _recv_bytes(self, cnt):
data = self.recvall(cnt)
return compat_struct_unpack('!{0}B'.format(cnt), data)
@staticmethod
def _len_and_data(data):
return compat_struct_pack('!B', len(data)) + data
def _check_response_version(self, expected_version, got_version):
if got_version != expected_version:
self.close()
raise InvalidVersionError(expected_version, got_version)
def _resolve_address(self, destaddr, default, use_remote_dns):
try:
return socket.inet_aton(destaddr)
except socket.error:
if use_remote_dns and self._proxy.remote_dns:
return default
else:
return socket.inet_aton(socket.gethostbyname(destaddr))
def _setup_socks4(self, address, is_4a=False):
destaddr, port = address
ipaddr = self._resolve_address(destaddr, SOCKS4_DEFAULT_DSTIP, use_remote_dns=is_4a)
packet = compat_struct_pack('!BBH', SOCKS4_VERSION, Socks4Command.CMD_CONNECT, port) + ipaddr
username = (self._proxy.username or '').encode('utf-8')
packet += username + b'\x00'
if is_4a and self._proxy.remote_dns:
packet += destaddr.encode('utf-8') + b'\x00'
self.sendall(packet)
version, resp_code, dstport, dsthost = compat_struct_unpack('!BBHI', self.recvall(8))
self._check_response_version(SOCKS4_REPLY_VERSION, version)
if resp_code != Socks4Error.ERR_SUCCESS:
self.close()
raise Socks4Error(resp_code)
return (dsthost, dstport)
def _setup_socks4a(self, address):
self._setup_socks4(address, is_4a=True)
def _socks5_auth(self):
packet = compat_struct_pack('!B', SOCKS5_VERSION)
auth_methods = [Socks5Auth.AUTH_NONE]
if self._proxy.username and self._proxy.password:
auth_methods.append(Socks5Auth.AUTH_USER_PASS)
packet += compat_struct_pack('!B', len(auth_methods))
packet += compat_struct_pack('!{0}B'.format(len(auth_methods)), *auth_methods)
self.sendall(packet)
version, method = self._recv_bytes(2)
self._check_response_version(SOCKS5_VERSION, version)
if method == Socks5Auth.AUTH_NO_ACCEPTABLE or (
method == Socks5Auth.AUTH_USER_PASS and (not self._proxy.username or not self._proxy.password)):
self.close()
raise Socks5Error(Socks5Auth.AUTH_NO_ACCEPTABLE)
if method == Socks5Auth.AUTH_USER_PASS:
username = self._proxy.username.encode('utf-8')
password = self._proxy.password.encode('utf-8')
packet = compat_struct_pack('!B', SOCKS5_USER_AUTH_VERSION)
packet += self._len_and_data(username) + self._len_and_data(password)
self.sendall(packet)
version, status = self._recv_bytes(2)
self._check_response_version(SOCKS5_USER_AUTH_VERSION, version)
if status != SOCKS5_USER_AUTH_SUCCESS:
self.close()
raise Socks5Error(Socks5Error.ERR_GENERAL_FAILURE)
def _setup_socks5(self, address):
destaddr, port = address
ipaddr = self._resolve_address(destaddr, None, use_remote_dns=True)
self._socks5_auth()
reserved = 0
packet = compat_struct_pack('!BBB', SOCKS5_VERSION, Socks5Command.CMD_CONNECT, reserved)
if ipaddr is None:
destaddr = destaddr.encode('utf-8')
packet += compat_struct_pack('!B', Socks5AddressType.ATYP_DOMAINNAME)
packet += self._len_and_data(destaddr)
else:
packet += compat_struct_pack('!B', Socks5AddressType.ATYP_IPV4) + ipaddr
packet += compat_struct_pack('!H', port)
self.sendall(packet)
version, status, reserved, atype = self._recv_bytes(4)
self._check_response_version(SOCKS5_VERSION, version)
if status != Socks5Error.ERR_SUCCESS:
self.close()
raise Socks5Error(status)
if atype == Socks5AddressType.ATYP_IPV4:
destaddr = self.recvall(4)
elif atype == Socks5AddressType.ATYP_DOMAINNAME:
alen = compat_ord(self.recv(1))
destaddr = self.recvall(alen)
elif atype == Socks5AddressType.ATYP_IPV6:
destaddr = self.recvall(16)
destport = compat_struct_unpack('!H', self.recvall(2))[0]
return (destaddr, destport)
def _make_proxy(self, connect_func, address):
if not self._proxy:
return connect_func(self, address)
result = connect_func(self, (self._proxy.host, self._proxy.port))
if result != 0 and result is not None:
return result
setup_funcs = {
ProxyType.SOCKS4: self._setup_socks4,
ProxyType.SOCKS4A: self._setup_socks4a,
ProxyType.SOCKS5: self._setup_socks5,
}
setup_funcs[self._proxy.type](address)
return result
def connect(self, address):
self._make_proxy(socket.socket.connect, address)
def connect_ex(self, address):
return self._make_proxy(socket.socket.connect_ex, address)
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/utils.py | youtube_dl/utils.py | #!/usr/bin/env python
# coding: utf-8
from __future__ import unicode_literals
import base64
import binascii
import calendar
import codecs
import collections
import contextlib
import ctypes
import datetime
import email.utils
import email.header
import errno
import functools
import inspect
import io
import itertools
import json
import locale
import math
import operator
import os
import platform
import random
import re
import socket
import ssl
import subprocess
import sys
import tempfile
import time
import traceback
import unicodedata
import xml.etree.ElementTree
import zlib
from .compat import (
compat_HTMLParseError,
compat_HTMLParser,
compat_basestring,
compat_brotli as brotli,
compat_casefold,
compat_chr,
compat_collections_abc,
compat_contextlib_suppress,
compat_cookiejar,
compat_ctypes_WINFUNCTYPE,
compat_datetime_timedelta_total_seconds,
compat_etree_Element,
compat_etree_fromstring,
compat_etree_iterfind,
compat_expanduser,
compat_filter as filter,
compat_filter_fns,
compat_html_entities,
compat_html_entities_html5,
compat_http_client,
compat_http_cookies,
compat_integer_types,
compat_kwargs,
compat_ncompress as ncompress,
compat_os_name,
compat_re_Match,
compat_re_Pattern,
compat_shlex_quote,
compat_str,
compat_struct_pack,
compat_struct_unpack,
compat_urllib_error,
compat_urllib_HTTPError,
compat_urllib_parse,
compat_urllib_parse_parse_qs as compat_parse_qs,
compat_urllib_parse_urlencode,
compat_urllib_parse_urlparse,
compat_urllib_parse_unquote_plus,
compat_urllib_request,
compat_xpath,
)
from .socks import (
ProxyType,
sockssocket,
)
def register_socks_protocols():
# "Register" SOCKS protocols
# In Python < 2.6.5, urlsplit() suffers from bug https://bugs.python.org/issue7904
# URLs with protocols not in urlparse.uses_netloc are not handled correctly
for scheme in ('socks', 'socks4', 'socks4a', 'socks5'):
if scheme not in compat_urllib_parse.uses_netloc:
compat_urllib_parse.uses_netloc.append(scheme)
# Unfavoured alias
compiled_regex_type = compat_re_Pattern
def random_user_agent():
_USER_AGENT_TPL = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/%s Safari/537.36'
_CHROME_VERSIONS = (
'74.0.3729.129',
'76.0.3780.3',
'76.0.3780.2',
'74.0.3729.128',
'76.0.3780.1',
'76.0.3780.0',
'75.0.3770.15',
'74.0.3729.127',
'74.0.3729.126',
'76.0.3779.1',
'76.0.3779.0',
'75.0.3770.14',
'74.0.3729.125',
'76.0.3778.1',
'76.0.3778.0',
'75.0.3770.13',
'74.0.3729.124',
'74.0.3729.123',
'73.0.3683.121',
'76.0.3777.1',
'76.0.3777.0',
'75.0.3770.12',
'74.0.3729.122',
'76.0.3776.4',
'75.0.3770.11',
'74.0.3729.121',
'76.0.3776.3',
'76.0.3776.2',
'73.0.3683.120',
'74.0.3729.120',
'74.0.3729.119',
'74.0.3729.118',
'76.0.3776.1',
'76.0.3776.0',
'76.0.3775.5',
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| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | true |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/swfinterp.py | youtube_dl/swfinterp.py | from __future__ import unicode_literals
import collections
import io
import zlib
from .compat import (
compat_str,
compat_struct_unpack,
)
from .utils import (
ExtractorError,
)
def _extract_tags(file_contents):
if file_contents[1:3] != b'WS':
raise ExtractorError(
'Not an SWF file; header is %r' % file_contents[:3])
if file_contents[:1] == b'C':
content = zlib.decompress(file_contents[8:])
else:
raise NotImplementedError(
'Unsupported compression format %r' %
file_contents[:1])
# Determine number of bits in framesize rectangle
framesize_nbits = compat_struct_unpack('!B', content[:1])[0] >> 3
framesize_len = (5 + 4 * framesize_nbits + 7) // 8
pos = framesize_len + 2 + 2
while pos < len(content):
header16 = compat_struct_unpack('<H', content[pos:pos + 2])[0]
pos += 2
tag_code = header16 >> 6
tag_len = header16 & 0x3f
if tag_len == 0x3f:
tag_len = compat_struct_unpack('<I', content[pos:pos + 4])[0]
pos += 4
assert pos + tag_len <= len(content), \
('Tag %d ends at %d+%d - that\'s longer than the file (%d)'
% (tag_code, pos, tag_len, len(content)))
yield (tag_code, content[pos:pos + tag_len])
pos += tag_len
class _AVMClass_Object(object):
def __init__(self, avm_class):
self.avm_class = avm_class
def __repr__(self):
return '%s#%x' % (self.avm_class.name, id(self))
class _ScopeDict(dict):
def __init__(self, avm_class):
super(_ScopeDict, self).__init__()
self.avm_class = avm_class
def __repr__(self):
return '%s__Scope(%s)' % (
self.avm_class.name,
super(_ScopeDict, self).__repr__())
class _AVMClass(object):
def __init__(self, name_idx, name, static_properties=None):
self.name_idx = name_idx
self.name = name
self.method_names = {}
self.method_idxs = {}
self.methods = {}
self.method_pyfunctions = {}
self.static_properties = static_properties if static_properties else {}
self.variables = _ScopeDict(self)
self.constants = {}
def make_object(self):
return _AVMClass_Object(self)
def __repr__(self):
return '_AVMClass(%s)' % (self.name)
def register_methods(self, methods):
self.method_names.update(methods.items())
self.method_idxs.update(dict(
(idx, name)
for name, idx in methods.items()))
class _Multiname(object):
def __init__(self, kind):
self.kind = kind
def __repr__(self):
return '[MULTINAME kind: 0x%x]' % self.kind
def _read_int(reader):
res = 0
shift = 0
for _ in range(5):
buf = reader.read(1)
assert len(buf) == 1
b = compat_struct_unpack('<B', buf)[0]
res = res | ((b & 0x7f) << shift)
if b & 0x80 == 0:
break
shift += 7
return res
def _u30(reader):
res = _read_int(reader)
assert res & 0xf0000000 == 0
return res
_u32 = _read_int
def _s32(reader):
v = _read_int(reader)
if v & 0x80000000 != 0:
v = - ((v ^ 0xffffffff) + 1)
return v
def _s24(reader):
bs = reader.read(3)
assert len(bs) == 3
last_byte = b'\xff' if (ord(bs[2:3]) >= 0x80) else b'\x00'
return compat_struct_unpack('<i', bs + last_byte)[0]
def _read_string(reader):
slen = _u30(reader)
resb = reader.read(slen)
assert len(resb) == slen
return resb.decode('utf-8')
def _read_bytes(count, reader):
assert count >= 0
resb = reader.read(count)
assert len(resb) == count
return resb
def _read_byte(reader):
resb = _read_bytes(1, reader=reader)
res = compat_struct_unpack('<B', resb)[0]
return res
StringClass = _AVMClass('(no name idx)', 'String')
ByteArrayClass = _AVMClass('(no name idx)', 'ByteArray')
TimerClass = _AVMClass('(no name idx)', 'Timer')
TimerEventClass = _AVMClass('(no name idx)', 'TimerEvent', {'TIMER': 'timer'})
_builtin_classes = {
StringClass.name: StringClass,
ByteArrayClass.name: ByteArrayClass,
TimerClass.name: TimerClass,
TimerEventClass.name: TimerEventClass,
}
class _Undefined(object):
def __bool__(self):
return False
__nonzero__ = __bool__
def __hash__(self):
return 0
def __str__(self):
return 'undefined'
__repr__ = __str__
undefined = _Undefined()
class SWFInterpreter(object):
def __init__(self, file_contents):
self._patched_functions = {
(TimerClass, 'addEventListener'): lambda params: undefined,
}
code_tag = next(tag
for tag_code, tag in _extract_tags(file_contents)
if tag_code == 82)
p = code_tag.index(b'\0', 4) + 1
code_reader = io.BytesIO(code_tag[p:])
# Parse ABC (AVM2 ByteCode)
# Define a couple convenience methods
u30 = lambda *args: _u30(*args, reader=code_reader)
s32 = lambda *args: _s32(*args, reader=code_reader)
u32 = lambda *args: _u32(*args, reader=code_reader)
read_bytes = lambda *args: _read_bytes(*args, reader=code_reader)
read_byte = lambda *args: _read_byte(*args, reader=code_reader)
# minor_version + major_version
read_bytes(2 + 2)
# Constant pool
int_count = u30()
self.constant_ints = [0]
for _c in range(1, int_count):
self.constant_ints.append(s32())
self.constant_uints = [0]
uint_count = u30()
for _c in range(1, uint_count):
self.constant_uints.append(u32())
double_count = u30()
read_bytes(max(0, (double_count - 1)) * 8)
string_count = u30()
self.constant_strings = ['']
for _c in range(1, string_count):
s = _read_string(code_reader)
self.constant_strings.append(s)
namespace_count = u30()
for _c in range(1, namespace_count):
read_bytes(1) # kind
u30() # name
ns_set_count = u30()
for _c in range(1, ns_set_count):
count = u30()
for _c2 in range(count):
u30()
multiname_count = u30()
MULTINAME_SIZES = {
0x07: 2, # QName
0x0d: 2, # QNameA
0x0f: 1, # RTQName
0x10: 1, # RTQNameA
0x11: 0, # RTQNameL
0x12: 0, # RTQNameLA
0x09: 2, # Multiname
0x0e: 2, # MultinameA
0x1b: 1, # MultinameL
0x1c: 1, # MultinameLA
}
self.multinames = ['']
for _c in range(1, multiname_count):
kind = u30()
assert kind in MULTINAME_SIZES, 'Invalid multiname kind %r' % kind
if kind == 0x07:
u30() # namespace_idx
name_idx = u30()
self.multinames.append(self.constant_strings[name_idx])
elif kind == 0x09:
name_idx = u30()
u30()
self.multinames.append(self.constant_strings[name_idx])
else:
self.multinames.append(_Multiname(kind))
for _c2 in range(MULTINAME_SIZES[kind]):
u30()
# Methods
method_count = u30()
MethodInfo = collections.namedtuple(
'MethodInfo',
['NEED_ARGUMENTS', 'NEED_REST'])
method_infos = []
for method_id in range(method_count):
param_count = u30()
u30() # return type
for _ in range(param_count):
u30() # param type
u30() # name index (always 0 for youtube)
flags = read_byte()
if flags & 0x08 != 0:
# Options present
option_count = u30()
for c in range(option_count):
u30() # val
read_bytes(1) # kind
if flags & 0x80 != 0:
# Param names present
for _ in range(param_count):
u30() # param name
mi = MethodInfo(flags & 0x01 != 0, flags & 0x04 != 0)
method_infos.append(mi)
# Metadata
metadata_count = u30()
for _c in range(metadata_count):
u30() # name
item_count = u30()
for _c2 in range(item_count):
u30() # key
u30() # value
def parse_traits_info():
trait_name_idx = u30()
kind_full = read_byte()
kind = kind_full & 0x0f
attrs = kind_full >> 4
methods = {}
constants = None
if kind == 0x00: # Slot
u30() # Slot id
u30() # type_name_idx
vindex = u30()
if vindex != 0:
read_byte() # vkind
elif kind == 0x06: # Const
u30() # Slot id
u30() # type_name_idx
vindex = u30()
vkind = 'any'
if vindex != 0:
vkind = read_byte()
if vkind == 0x03: # Constant_Int
value = self.constant_ints[vindex]
elif vkind == 0x04: # Constant_UInt
value = self.constant_uints[vindex]
else:
return {}, None # Ignore silently for now
constants = {self.multinames[trait_name_idx]: value}
elif kind in (0x01, 0x02, 0x03): # Method / Getter / Setter
u30() # disp_id
method_idx = u30()
methods[self.multinames[trait_name_idx]] = method_idx
elif kind == 0x04: # Class
u30() # slot_id
u30() # classi
elif kind == 0x05: # Function
u30() # slot_id
function_idx = u30()
methods[function_idx] = self.multinames[trait_name_idx]
else:
raise ExtractorError('Unsupported trait kind %d' % kind)
if attrs & 0x4 != 0: # Metadata present
metadata_count = u30()
for _c3 in range(metadata_count):
u30() # metadata index
return methods, constants
# Classes
class_count = u30()
classes = []
for class_id in range(class_count):
name_idx = u30()
cname = self.multinames[name_idx]
avm_class = _AVMClass(name_idx, cname)
classes.append(avm_class)
u30() # super_name idx
flags = read_byte()
if flags & 0x08 != 0: # Protected namespace is present
u30() # protected_ns_idx
intrf_count = u30()
for _c2 in range(intrf_count):
u30()
u30() # iinit
trait_count = u30()
for _c2 in range(trait_count):
trait_methods, trait_constants = parse_traits_info()
avm_class.register_methods(trait_methods)
if trait_constants:
avm_class.constants.update(trait_constants)
assert len(classes) == class_count
self._classes_by_name = dict((c.name, c) for c in classes)
for avm_class in classes:
avm_class.cinit_idx = u30()
trait_count = u30()
for _c2 in range(trait_count):
trait_methods, trait_constants = parse_traits_info()
avm_class.register_methods(trait_methods)
if trait_constants:
avm_class.constants.update(trait_constants)
# Scripts
script_count = u30()
for _c in range(script_count):
u30() # init
trait_count = u30()
for _c2 in range(trait_count):
parse_traits_info()
# Method bodies
method_body_count = u30()
Method = collections.namedtuple('Method', ['code', 'local_count'])
self._all_methods = []
for _c in range(method_body_count):
method_idx = u30()
u30() # max_stack
local_count = u30()
u30() # init_scope_depth
u30() # max_scope_depth
code_length = u30()
code = read_bytes(code_length)
m = Method(code, local_count)
self._all_methods.append(m)
for avm_class in classes:
if method_idx in avm_class.method_idxs:
avm_class.methods[avm_class.method_idxs[method_idx]] = m
exception_count = u30()
for _c2 in range(exception_count):
u30() # from
u30() # to
u30() # target
u30() # exc_type
u30() # var_name
trait_count = u30()
for _c2 in range(trait_count):
parse_traits_info()
assert p + code_reader.tell() == len(code_tag)
def patch_function(self, avm_class, func_name, f):
self._patched_functions[(avm_class, func_name)] = f
def extract_class(self, class_name, call_cinit=True):
try:
res = self._classes_by_name[class_name]
except KeyError:
raise ExtractorError('Class %r not found' % class_name)
if call_cinit and hasattr(res, 'cinit_idx'):
res.register_methods({'$cinit': res.cinit_idx})
res.methods['$cinit'] = self._all_methods[res.cinit_idx]
cinit = self.extract_function(res, '$cinit')
cinit([])
return res
def extract_function(self, avm_class, func_name):
p = self._patched_functions.get((avm_class, func_name))
if p:
return p
if func_name in avm_class.method_pyfunctions:
return avm_class.method_pyfunctions[func_name]
if func_name in self._classes_by_name:
return self._classes_by_name[func_name].make_object()
if func_name not in avm_class.methods:
raise ExtractorError('Cannot find function %s.%s' % (
avm_class.name, func_name))
m = avm_class.methods[func_name]
def resfunc(args):
# Helper functions
coder = io.BytesIO(m.code)
s24 = lambda: _s24(coder)
u30 = lambda: _u30(coder)
registers = [avm_class.variables] + list(args) + [None] * m.local_count
stack = []
scopes = collections.deque([
self._classes_by_name, avm_class.constants, avm_class.variables])
while True:
opcode = _read_byte(coder)
if opcode == 9: # label
pass # Spec says: "Do nothing."
elif opcode == 16: # jump
offset = s24()
coder.seek(coder.tell() + offset)
elif opcode == 17: # iftrue
offset = s24()
value = stack.pop()
if value:
coder.seek(coder.tell() + offset)
elif opcode == 18: # iffalse
offset = s24()
value = stack.pop()
if not value:
coder.seek(coder.tell() + offset)
elif opcode == 19: # ifeq
offset = s24()
value2 = stack.pop()
value1 = stack.pop()
if value2 == value1:
coder.seek(coder.tell() + offset)
elif opcode == 20: # ifne
offset = s24()
value2 = stack.pop()
value1 = stack.pop()
if value2 != value1:
coder.seek(coder.tell() + offset)
elif opcode == 21: # iflt
offset = s24()
value2 = stack.pop()
value1 = stack.pop()
if value1 < value2:
coder.seek(coder.tell() + offset)
elif opcode == 32: # pushnull
stack.append(None)
elif opcode == 33: # pushundefined
stack.append(undefined)
elif opcode == 36: # pushbyte
v = _read_byte(coder)
stack.append(v)
elif opcode == 37: # pushshort
v = u30()
stack.append(v)
elif opcode == 38: # pushtrue
stack.append(True)
elif opcode == 39: # pushfalse
stack.append(False)
elif opcode == 40: # pushnan
stack.append(float('NaN'))
elif opcode == 42: # dup
value = stack[-1]
stack.append(value)
elif opcode == 44: # pushstring
idx = u30()
stack.append(self.constant_strings[idx])
elif opcode == 48: # pushscope
new_scope = stack.pop()
scopes.append(new_scope)
elif opcode == 66: # construct
arg_count = u30()
args = list(reversed(
[stack.pop() for _ in range(arg_count)]))
obj = stack.pop()
res = obj.avm_class.make_object()
stack.append(res)
elif opcode == 70: # callproperty
index = u30()
mname = self.multinames[index]
arg_count = u30()
args = list(reversed(
[stack.pop() for _ in range(arg_count)]))
obj = stack.pop()
if obj == StringClass:
if mname == 'String':
assert len(args) == 1
assert isinstance(args[0], (
int, compat_str, _Undefined))
if args[0] == undefined:
res = 'undefined'
else:
res = compat_str(args[0])
stack.append(res)
continue
else:
raise NotImplementedError(
'Function String.%s is not yet implemented'
% mname)
elif isinstance(obj, _AVMClass_Object):
func = self.extract_function(obj.avm_class, mname)
res = func(args)
stack.append(res)
continue
elif isinstance(obj, _AVMClass):
func = self.extract_function(obj, mname)
res = func(args)
stack.append(res)
continue
elif isinstance(obj, _ScopeDict):
if mname in obj.avm_class.method_names:
func = self.extract_function(obj.avm_class, mname)
res = func(args)
else:
res = obj[mname]
stack.append(res)
continue
elif isinstance(obj, compat_str):
if mname == 'split':
assert len(args) == 1
assert isinstance(args[0], compat_str)
if args[0] == '':
res = list(obj)
else:
res = obj.split(args[0])
stack.append(res)
continue
elif mname == 'charCodeAt':
assert len(args) <= 1
idx = 0 if len(args) == 0 else args[0]
assert isinstance(idx, int)
res = ord(obj[idx])
stack.append(res)
continue
elif isinstance(obj, list):
if mname == 'slice':
assert len(args) == 1
assert isinstance(args[0], int)
res = obj[args[0]:]
stack.append(res)
continue
elif mname == 'join':
assert len(args) == 1
assert isinstance(args[0], compat_str)
res = args[0].join(obj)
stack.append(res)
continue
raise NotImplementedError(
'Unsupported property %r on %r'
% (mname, obj))
elif opcode == 71: # returnvoid
res = undefined
return res
elif opcode == 72: # returnvalue
res = stack.pop()
return res
elif opcode == 73: # constructsuper
# Not yet implemented, just hope it works without it
arg_count = u30()
args = list(reversed(
[stack.pop() for _ in range(arg_count)]))
obj = stack.pop()
elif opcode == 74: # constructproperty
index = u30()
arg_count = u30()
args = list(reversed(
[stack.pop() for _ in range(arg_count)]))
obj = stack.pop()
mname = self.multinames[index]
assert isinstance(obj, _AVMClass)
# We do not actually call the constructor for now;
# we just pretend it does nothing
stack.append(obj.make_object())
elif opcode == 79: # callpropvoid
index = u30()
mname = self.multinames[index]
arg_count = u30()
args = list(reversed(
[stack.pop() for _ in range(arg_count)]))
obj = stack.pop()
if isinstance(obj, _AVMClass_Object):
func = self.extract_function(obj.avm_class, mname)
res = func(args)
assert res is undefined
continue
if isinstance(obj, _ScopeDict):
assert mname in obj.avm_class.method_names
func = self.extract_function(obj.avm_class, mname)
res = func(args)
assert res is undefined
continue
if mname == 'reverse':
assert isinstance(obj, list)
obj.reverse()
else:
raise NotImplementedError(
'Unsupported (void) property %r on %r'
% (mname, obj))
elif opcode == 86: # newarray
arg_count = u30()
arr = []
for i in range(arg_count):
arr.append(stack.pop())
arr = arr[::-1]
stack.append(arr)
elif opcode == 93: # findpropstrict
index = u30()
mname = self.multinames[index]
for s in reversed(scopes):
if mname in s:
res = s
break
else:
res = scopes[0]
if mname not in res and mname in _builtin_classes:
stack.append(_builtin_classes[mname])
else:
stack.append(res[mname])
elif opcode == 94: # findproperty
index = u30()
mname = self.multinames[index]
for s in reversed(scopes):
if mname in s:
res = s
break
else:
res = avm_class.variables
stack.append(res)
elif opcode == 96: # getlex
index = u30()
mname = self.multinames[index]
for s in reversed(scopes):
if mname in s:
scope = s
break
else:
scope = avm_class.variables
if mname in scope:
res = scope[mname]
elif mname in _builtin_classes:
res = _builtin_classes[mname]
else:
# Assume uninitialized
# TODO warn here
res = undefined
stack.append(res)
elif opcode == 97: # setproperty
index = u30()
value = stack.pop()
idx = self.multinames[index]
if isinstance(idx, _Multiname):
idx = stack.pop()
obj = stack.pop()
obj[idx] = value
elif opcode == 98: # getlocal
index = u30()
stack.append(registers[index])
elif opcode == 99: # setlocal
index = u30()
value = stack.pop()
registers[index] = value
elif opcode == 102: # getproperty
index = u30()
pname = self.multinames[index]
if pname == 'length':
obj = stack.pop()
assert isinstance(obj, (compat_str, list))
stack.append(len(obj))
elif isinstance(pname, compat_str): # Member access
obj = stack.pop()
if isinstance(obj, _AVMClass):
res = obj.static_properties[pname]
stack.append(res)
continue
assert isinstance(obj, (dict, _ScopeDict)), \
'Accessing member %r on %r' % (pname, obj)
res = obj.get(pname, undefined)
stack.append(res)
else: # Assume attribute access
idx = stack.pop()
assert isinstance(idx, int)
obj = stack.pop()
assert isinstance(obj, list)
stack.append(obj[idx])
elif opcode == 104: # initproperty
index = u30()
value = stack.pop()
idx = self.multinames[index]
if isinstance(idx, _Multiname):
idx = stack.pop()
obj = stack.pop()
obj[idx] = value
elif opcode == 115: # convert_
value = stack.pop()
intvalue = int(value)
stack.append(intvalue)
elif opcode == 128: # coerce
u30()
elif opcode == 130: # coerce_a
value = stack.pop()
# um, yes, it's any value
stack.append(value)
elif opcode == 133: # coerce_s
assert isinstance(stack[-1], (type(None), compat_str))
elif opcode == 147: # decrement
value = stack.pop()
assert isinstance(value, int)
stack.append(value - 1)
elif opcode == 149: # typeof
value = stack.pop()
return {
_Undefined: 'undefined',
compat_str: 'String',
int: 'Number',
float: 'Number',
}[type(value)]
elif opcode == 160: # add
value2 = stack.pop()
value1 = stack.pop()
res = value1 + value2
stack.append(res)
elif opcode == 161: # subtract
value2 = stack.pop()
value1 = stack.pop()
res = value1 - value2
stack.append(res)
elif opcode == 162: # multiply
value2 = stack.pop()
value1 = stack.pop()
res = value1 * value2
stack.append(res)
elif opcode == 164: # modulo
value2 = stack.pop()
value1 = stack.pop()
res = value1 % value2
stack.append(res)
elif opcode == 168: # bitand
value2 = stack.pop()
value1 = stack.pop()
assert isinstance(value1, int)
assert isinstance(value2, int)
res = value1 & value2
stack.append(res)
elif opcode == 171: # equals
value2 = stack.pop()
value1 = stack.pop()
result = value1 == value2
stack.append(result)
elif opcode == 175: # greaterequals
value2 = stack.pop()
value1 = stack.pop()
result = value1 >= value2
stack.append(result)
elif opcode == 192: # increment_i
value = stack.pop()
assert isinstance(value, int)
stack.append(value + 1)
elif opcode == 208: # getlocal_0
stack.append(registers[0])
elif opcode == 209: # getlocal_1
stack.append(registers[1])
elif opcode == 210: # getlocal_2
stack.append(registers[2])
elif opcode == 211: # getlocal_3
stack.append(registers[3])
elif opcode == 212: # setlocal_0
registers[0] = stack.pop()
elif opcode == 213: # setlocal_1
registers[1] = stack.pop()
elif opcode == 214: # setlocal_2
registers[2] = stack.pop()
elif opcode == 215: # setlocal_3
registers[3] = stack.pop()
else:
raise NotImplementedError(
'Unsupported opcode %d' % opcode)
avm_class.method_pyfunctions[func_name] = resfunc
return resfunc
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/jsinterp.py | youtube_dl/jsinterp.py | # coding: utf-8
from __future__ import unicode_literals
import calendar
import itertools
import json
import operator
import re
import time
from functools import update_wrapper, wraps
from .utils import (
error_to_compat_str,
ExtractorError,
float_or_none,
int_or_none,
js_to_json,
remove_quotes,
str_or_none,
unified_timestamp,
variadic,
write_string,
)
from .compat import (
compat_basestring,
compat_chr,
compat_collections_chain_map as ChainMap,
compat_contextlib_suppress,
compat_filter as filter,
compat_int,
compat_integer_types,
compat_itertools_zip_longest as zip_longest,
compat_map as map,
compat_numeric_types,
compat_str,
)
# name JS functions
class function_with_repr(object):
# from yt_dlp/utils.py, but in this module
# repr_ is always set
def __init__(self, func, repr_):
update_wrapper(self, func)
self.func, self.__repr = func, repr_
def __call__(self, *args, **kwargs):
return self.func(*args, **kwargs)
def __repr__(self):
return self.__repr
# name JS operators
def wraps_op(op):
def update_and_rename_wrapper(w):
f = update_wrapper(w, op)
# fn names are str in both Py 2/3
f.__name__ = str('JS_') + f.__name__
return f
return update_and_rename_wrapper
# NB In principle NaN cannot be checked by membership.
# Here all NaN values are actually this one, so _NaN is _NaN,
# although _NaN != _NaN. Ditto Infinity.
_NaN = float('nan')
_Infinity = float('inf')
class JS_Undefined(object):
pass
def _js_bit_op(op, is_shift=False):
def zeroise(x, is_shift_arg=False):
if isinstance(x, compat_integer_types):
return (x % 32) if is_shift_arg else (x & 0xffffffff)
try:
x = float(x)
if is_shift_arg:
x = int(x % 32)
elif x < 0:
x = -compat_int(-x % 0xffffffff)
else:
x = compat_int(x % 0xffffffff)
except (ValueError, TypeError):
# also here for int(NaN), including float('inf') % 32
x = 0
return x
@wraps_op(op)
def wrapped(a, b):
return op(zeroise(a), zeroise(b, is_shift)) & 0xffffffff
return wrapped
def _js_arith_op(op, div=False):
@wraps_op(op)
def wrapped(a, b):
if JS_Undefined in (a, b):
return _NaN
# null, "" --> 0
a, b = (float_or_none(
(x.strip() if isinstance(x, compat_basestring) else x) or 0,
default=_NaN) for x in (a, b))
if _NaN in (a, b):
return _NaN
try:
return op(a, b)
except ZeroDivisionError:
return _NaN if not (div and (a or b)) else _Infinity
return wrapped
_js_arith_add = _js_arith_op(operator.add)
def _js_add(a, b):
if not (isinstance(a, compat_basestring) or isinstance(b, compat_basestring)):
return _js_arith_add(a, b)
if not isinstance(a, compat_basestring):
a = _js_toString(a)
elif not isinstance(b, compat_basestring):
b = _js_toString(b)
return operator.concat(a, b)
_js_mod = _js_arith_op(operator.mod)
__js_exp = _js_arith_op(operator.pow)
def _js_exp(a, b):
if not b:
return 1 # even 0 ** 0 !!
return __js_exp(a, b)
def _js_to_primitive(v):
return (
','.join(map(_js_toString, v)) if isinstance(v, list)
else '[object Object]' if isinstance(v, dict)
else compat_str(v) if not isinstance(v, (
compat_numeric_types, compat_basestring))
else v
)
# more exact: yt-dlp/yt-dlp#12110
def _js_toString(v):
return (
'undefined' if v is JS_Undefined
else 'Infinity' if v == _Infinity
else 'NaN' if v is _NaN
else 'null' if v is None
# bool <= int: do this first
else ('false', 'true')[v] if isinstance(v, bool)
else re.sub(r'(?<=\d)\.?0*$', '', '{0:.7f}'.format(v)) if isinstance(v, compat_numeric_types)
else _js_to_primitive(v))
_nullish = frozenset((None, JS_Undefined))
def _js_eq(a, b):
# NaN != any
if _NaN in (a, b):
return False
# Object is Object
if isinstance(a, type(b)) and isinstance(b, (dict, list)):
return operator.is_(a, b)
# general case
if a == b:
return True
# null == undefined
a_b = set((a, b))
if a_b & _nullish:
return a_b <= _nullish
a, b = _js_to_primitive(a), _js_to_primitive(b)
if not isinstance(a, compat_basestring):
a, b = b, a
# Number to String: convert the string to a number
# Conversion failure results in ... false
if isinstance(a, compat_basestring):
return float_or_none(a) == b
return a == b
def _js_neq(a, b):
return not _js_eq(a, b)
def _js_id_op(op):
@wraps_op(op)
def wrapped(a, b):
if _NaN in (a, b):
return op(_NaN, None)
if not isinstance(a, (compat_basestring, compat_numeric_types)):
a, b = b, a
# strings are === if ==
# why 'a' is not 'a': https://stackoverflow.com/a/1504848
if isinstance(a, (compat_basestring, compat_numeric_types)):
return a == b if op(0, 0) else a != b
return op(a, b)
return wrapped
def _js_comp_op(op):
@wraps_op(op)
def wrapped(a, b):
if JS_Undefined in (a, b):
return False
if isinstance(a, compat_basestring):
b = compat_str(b or 0)
elif isinstance(b, compat_basestring):
a = compat_str(a or 0)
return op(a or 0, b or 0)
return wrapped
def _js_ternary(cndn, if_true=True, if_false=False):
"""Simulate JS's ternary operator (cndn?if_true:if_false)"""
if cndn in (False, None, 0, '', JS_Undefined, _NaN):
return if_false
return if_true
def _js_unary_op(op):
@wraps_op(op)
def wrapped(a, _):
return op(a)
return wrapped
# https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/typeof
def _js_typeof(expr):
with compat_contextlib_suppress(TypeError, KeyError):
return {
JS_Undefined: 'undefined',
_NaN: 'number',
_Infinity: 'number',
True: 'boolean',
False: 'boolean',
None: 'object',
}[expr]
for t, n in (
(compat_basestring, 'string'),
(compat_numeric_types, 'number'),
):
if isinstance(expr, t):
return n
if callable(expr):
return 'function'
# TODO: Symbol, BigInt
return 'object'
# (op, definition) in order of binding priority, tightest first
# avoid dict to maintain order
# definition None => Defined in JSInterpreter._operator
_OPERATORS = (
('>>', _js_bit_op(operator.rshift, True)),
('<<', _js_bit_op(operator.lshift, True)),
('+', _js_add),
('-', _js_arith_op(operator.sub)),
('*', _js_arith_op(operator.mul)),
('%', _js_mod),
('/', _js_arith_op(operator.truediv, div=True)),
('**', _js_exp),
)
_LOG_OPERATORS = (
('|', _js_bit_op(operator.or_)),
('^', _js_bit_op(operator.xor)),
('&', _js_bit_op(operator.and_)),
)
_SC_OPERATORS = (
('?', None),
('??', None),
('||', None),
('&&', None),
)
_UNARY_OPERATORS_X = (
('void', _js_unary_op(lambda _: JS_Undefined)),
('typeof', _js_unary_op(_js_typeof)),
# avoid functools.partial here since Py2 update_wrapper(partial) -> no __module__
('!', _js_unary_op(lambda x: _js_ternary(x, if_true=False, if_false=True))),
)
_COMP_OPERATORS = (
('===', _js_id_op(operator.is_)),
('!==', _js_id_op(operator.is_not)),
('==', _js_eq),
('!=', _js_neq),
('<=', _js_comp_op(operator.le)),
('>=', _js_comp_op(operator.ge)),
('<', _js_comp_op(operator.lt)),
('>', _js_comp_op(operator.gt)),
)
_OPERATOR_RE = '|'.join(map(lambda x: re.escape(x[0]), _OPERATORS + _LOG_OPERATORS + _SC_OPERATORS))
_NAME_RE = r'[a-zA-Z_$][\w$]*'
_MATCHING_PARENS = dict(zip(*zip('()', '{}', '[]')))
_QUOTES = '\'"/'
_NESTED_BRACKETS = r'[^[\]]+(?:\[[^[\]]+(?:\[[^\]]+\])?\])?'
class JS_Break(ExtractorError):
def __init__(self):
ExtractorError.__init__(self, 'Invalid break')
class JS_Continue(ExtractorError):
def __init__(self):
ExtractorError.__init__(self, 'Invalid continue')
class JS_Throw(ExtractorError):
def __init__(self, e):
self.error = e
ExtractorError.__init__(self, 'Uncaught exception ' + error_to_compat_str(e))
class LocalNameSpace(ChainMap):
def __getitem__(self, key):
try:
return super(LocalNameSpace, self).__getitem__(key)
except KeyError:
return JS_Undefined
def __setitem__(self, key, value):
for scope in self.maps:
if key in scope:
scope[key] = value
return
self.maps[0][key] = value
def __delitem__(self, key):
raise NotImplementedError('Deleting is not supported')
def __repr__(self):
return 'LocalNameSpace({0!r})'.format(self.maps)
class Debugger(object):
ENABLED = False
@staticmethod
def write(*args, **kwargs):
level = kwargs.get('level', 100)
def truncate_string(s, left, right=0):
if s is None or len(s) <= left + right:
return s
return '...'.join((s[:left - 3], s[-right:] if right else ''))
write_string('[debug] JS: {0}{1}\n'.format(
' ' * (100 - level),
' '.join(truncate_string(compat_str(x), 50, 50) for x in args)))
@classmethod
def wrap_interpreter(cls, f):
if not cls.ENABLED:
return f
@wraps(f)
def interpret_statement(self, stmt, local_vars, allow_recursion, *args, **kwargs):
if cls.ENABLED and stmt.strip():
cls.write(stmt, level=allow_recursion)
try:
ret, should_ret = f(self, stmt, local_vars, allow_recursion, *args, **kwargs)
except Exception as e:
if cls.ENABLED:
if isinstance(e, ExtractorError):
e = e.orig_msg
cls.write('=> Raises:', e, '<-|', stmt, level=allow_recursion)
raise
if cls.ENABLED and stmt.strip():
if should_ret or repr(ret) != stmt:
cls.write(['->', '=>'][bool(should_ret)], repr(ret), '<-|', stmt, level=allow_recursion)
return ret, should_ret
return interpret_statement
class JSInterpreter(object):
__named_object_counter = 0
_OBJ_NAME = '__youtube_dl_jsinterp_obj'
OP_CHARS = None
def __init__(self, code, objects=None):
self.code, self._functions = code, {}
self._objects = {} if objects is None else objects
if type(self).OP_CHARS is None:
type(self).OP_CHARS = self.OP_CHARS = self.__op_chars()
class Exception(ExtractorError):
def __init__(self, msg, *args, **kwargs):
expr = kwargs.pop('expr', None)
msg = str_or_none(msg, default='"None"')
if expr is not None:
msg = '{0} in: {1!r:.100}'.format(msg.rstrip(), expr)
super(JSInterpreter.Exception, self).__init__(msg, *args, **kwargs)
class JS_Object(object):
def __getitem__(self, key):
if hasattr(self, key):
return getattr(self, key)
raise KeyError(key)
def dump(self):
"""Serialise the instance"""
raise NotImplementedError
class JS_RegExp(JS_Object):
RE_FLAGS = {
# special knowledge: Python's re flags are bitmask values, current max 128
# invent new bitmask values well above that for literal parsing
# JS 'u' flag is effectively always set (surrogate pairs aren't seen),
# but \u{...} and \p{...} escapes aren't handled); no additional JS 'v'
# features are supported
# TODO: execute matches with these flags (remaining: d, y)
'd': 1024, # Generate indices for substring matches
'g': 2048, # Global search
'i': re.I, # Case-insensitive search
'm': re.M, # Multi-line search
's': re.S, # Allows . to match newline characters
'u': re.U, # Treat a pattern as a sequence of unicode code points
'v': re.U, # Like 'u' with extended character class and \p{} syntax
'y': 4096, # Perform a "sticky" search that matches starting at the current position in the target string
}
def __init__(self, pattern_txt, flags=0):
if isinstance(flags, compat_str):
flags, _ = self.regex_flags(flags)
self.__self = None
pattern_txt = str_or_none(pattern_txt) or '(?:)'
# escape unintended embedded flags
pattern_txt = re.sub(
r'(\(\?)([aiLmsux]*)(-[imsx]+:|(?<!\?)\))',
lambda m: ''.join(
(re.escape(m.group(1)), m.group(2), re.escape(m.group(3)))
if m.group(3) == ')'
else ('(?:', m.group(2), m.group(3))),
pattern_txt)
# Avoid https://github.com/python/cpython/issues/74534
self.source = pattern_txt.replace('[[', r'[\[')
self.__flags = flags
def __instantiate(self):
if self.__self:
return
self.__self = re.compile(self.source, self.__flags)
# Thx: https://stackoverflow.com/questions/44773522/setattr-on-python2-sre-sre-pattern
for name in dir(self.__self):
# Only these? Obviously __class__, __init__.
# PyPy creates a __weakref__ attribute with value None
# that can't be setattr'd but also can't need to be copied.
if name in ('__class__', '__init__', '__weakref__'):
continue
if name == 'flags':
setattr(self, name, getattr(self.__self, name, self.__flags))
else:
setattr(self, name, getattr(self.__self, name))
def __getattr__(self, name):
self.__instantiate()
if name == 'pattern':
self.pattern = self.source
return self.pattern
elif hasattr(self.__self, name):
v = getattr(self.__self, name)
setattr(self, name, v)
return v
elif name in ('groupindex', 'groups'):
return 0 if name == 'groupindex' else {}
else:
flag_attrs = ( # order by 2nd elt
('hasIndices', 'd'),
('global', 'g'),
('ignoreCase', 'i'),
('multiline', 'm'),
('dotAll', 's'),
('unicode', 'u'),
('unicodeSets', 'v'),
('sticky', 'y'),
)
for k, c in flag_attrs:
if name == k:
return bool(self.RE_FLAGS[c] & self.__flags)
else:
if name == 'flags':
return ''.join(
(c if self.RE_FLAGS[c] & self.__flags else '')
for _, c in flag_attrs)
raise AttributeError('{0} has no attribute named {1}'.format(self, name))
@classmethod
def regex_flags(cls, expr):
flags = 0
if not expr:
return flags, expr
for idx, ch in enumerate(expr):
if ch not in cls.RE_FLAGS:
break
flags |= cls.RE_FLAGS[ch]
return flags, expr[idx + 1:]
def dump(self):
return '(/{0}/{1})'.format(
re.sub(r'(?<!\\)/', r'\/', self.source),
self.flags)
@staticmethod
def escape(string_):
return re.escape(string_)
class JS_Date(JS_Object):
_t = None
@staticmethod
def __ymd_etc(*args, **kw_is_utc):
# args: year, monthIndex, day, hours, minutes, seconds, milliseconds
is_utc = kw_is_utc.get('is_utc', False)
args = list(args[:7])
args += [0] * (9 - len(args))
args[1] += 1 # month 0..11 -> 1..12
ms = args[6]
for i in range(6, 9):
args[i] = -1 # don't know
if is_utc:
args[-1] = 1
# TODO: [MDN] When a segment overflows or underflows its expected
# range, it usually "carries over to" or "borrows from" the higher segment.
try:
mktime = calendar.timegm if is_utc else time.mktime
return mktime(time.struct_time(args)) * 1000 + ms
except (OverflowError, ValueError):
return None
@classmethod
def UTC(cls, *args):
t = cls.__ymd_etc(*args, is_utc=True)
return _NaN if t is None else t
@staticmethod
def parse(date_str, **kw_is_raw):
is_raw = kw_is_raw.get('is_raw', False)
t = unified_timestamp(str_or_none(date_str), False)
return int(t * 1000) if t is not None else t if is_raw else _NaN
@staticmethod
def now(**kw_is_raw):
is_raw = kw_is_raw.get('is_raw', False)
t = time.time()
return int(t * 1000) if t is not None else t if is_raw else _NaN
def __init__(self, *args):
if not args:
args = [self.now(is_raw=True)]
if len(args) == 1:
if isinstance(args[0], JSInterpreter.JS_Date):
self._t = int_or_none(args[0].valueOf(), default=None)
else:
arg_type = _js_typeof(args[0])
if arg_type == 'string':
self._t = self.parse(args[0], is_raw=True)
elif arg_type == 'number':
self._t = int(args[0])
else:
self._t = self.__ymd_etc(*args)
def toString(self):
try:
return time.strftime('%a %b %0d %Y %H:%M:%S %Z%z', self._t).rstrip()
except TypeError:
return "Invalid Date"
def valueOf(self):
return _NaN if self._t is None else self._t
def dump(self):
return '(new Date({0}))'.format(self.toString())
@classmethod
def __op_chars(cls):
op_chars = set(';,[')
for op in cls._all_operators():
if op[0].isalpha():
continue
op_chars.update(op[0])
return op_chars
def _named_object(self, namespace, obj):
self.__named_object_counter += 1
name = '%s%d' % (self._OBJ_NAME, self.__named_object_counter)
if callable(obj) and not isinstance(obj, function_with_repr):
obj = function_with_repr(obj, 'F<%s>' % (self.__named_object_counter, ))
namespace[name] = obj
return name
@classmethod
def _separate(cls, expr, delim=',', max_split=None, skip_delims=None):
if not expr:
return
# collections.Counter() is ~10% slower in both 2.7 and 3.9
counters = dict((k, 0) for k in _MATCHING_PARENS.values())
start, splits, pos, delim_len = 0, 0, 0, len(delim) - 1
in_quote, escaping, after_op, in_regex_char_group = None, False, True, False
skipping = 0
if skip_delims:
skip_delims = variadic(skip_delims)
skip_txt = None
for idx, char in enumerate(expr):
if skip_txt and idx <= skip_txt[1]:
continue
paren_delta = 0
if not in_quote:
if char == '/' and expr[idx:idx + 2] == '/*':
# skip a comment
skip_txt = expr[idx:].find('*/', 2)
skip_txt = [idx, idx + skip_txt + 1] if skip_txt >= 2 else None
if skip_txt:
continue
if char in _MATCHING_PARENS:
counters[_MATCHING_PARENS[char]] += 1
paren_delta = 1
elif char in counters:
counters[char] -= 1
paren_delta = -1
if not escaping:
if char in _QUOTES and in_quote in (char, None):
if in_quote or after_op or char != '/':
in_quote = None if in_quote and not in_regex_char_group else char
elif in_quote == '/' and char in '[]':
in_regex_char_group = char == '['
escaping = not escaping and in_quote and char == '\\'
after_op = not in_quote and (char in cls.OP_CHARS or paren_delta > 0 or (after_op and char.isspace()))
if char != delim[pos] or any(counters.values()) or in_quote:
pos = skipping = 0
continue
elif skipping > 0:
skipping -= 1
continue
elif pos == 0 and skip_delims:
here = expr[idx:]
for s in skip_delims:
if here.startswith(s) and s:
skipping = len(s) - 1
break
if skipping > 0:
continue
if pos < delim_len:
pos += 1
continue
if skip_txt and skip_txt[0] >= start and skip_txt[1] <= idx - delim_len:
yield expr[start:skip_txt[0]] + expr[skip_txt[1] + 1: idx - delim_len]
else:
yield expr[start: idx - delim_len]
skip_txt = None
start, pos = idx + 1, 0
splits += 1
if max_split and splits >= max_split:
break
if skip_txt and skip_txt[0] >= start:
yield expr[start:skip_txt[0]] + expr[skip_txt[1] + 1:]
else:
yield expr[start:]
@classmethod
def _separate_at_paren(cls, expr, delim=None):
if delim is None:
delim = expr and _MATCHING_PARENS[expr[0]]
separated = list(cls._separate(expr, delim, 1))
if len(separated) < 2:
raise cls.Exception('No terminating paren {delim} in {expr!r:.5500}'.format(**locals()))
return separated[0][1:].strip(), separated[1].strip()
@staticmethod
def _all_operators(_cached=[]):
if not _cached:
_cached.extend(itertools.chain(
# Ref: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Operators/Operator_Precedence
_SC_OPERATORS, _LOG_OPERATORS, _COMP_OPERATORS, _OPERATORS, _UNARY_OPERATORS_X))
return _cached
def _separate_at_op(self, expr, max_split=None):
for op, _ in self._all_operators():
# hackety: </> have higher priority than <</>>, but don't confuse them
skip_delim = (op + op) if op in '<>*?' else None
if op == '?':
skip_delim = (skip_delim, '?.')
separated = list(self._separate(expr, op, skip_delims=skip_delim))
if len(separated) < 2:
continue
right_expr = separated.pop()
# handle operators that are both unary and binary, minimal BODMAS
if op in ('+', '-'):
# simplify/adjust consecutive instances of these operators
undone = 0
separated = [s.strip() for s in separated]
while len(separated) > 1 and not separated[-1]:
undone += 1
separated.pop()
if op == '-' and undone % 2 != 0:
right_expr = op + right_expr
elif op == '+':
while len(separated) > 1 and set(separated[-1]) <= self.OP_CHARS:
right_expr = separated.pop() + right_expr
if separated[-1][-1:] in self.OP_CHARS:
right_expr = separated.pop() + right_expr
# hanging op at end of left => unary + (strip) or - (push right)
separated.append(right_expr)
dm_ops = ('*', '%', '/', '**')
dm_chars = set(''.join(dm_ops))
def yield_terms(s):
skip = False
for i, term in enumerate(s[:-1]):
if skip:
skip = False
continue
if not (dm_chars & set(term)):
yield term
continue
for dm_op in dm_ops:
bodmas = list(self._separate(term, dm_op, skip_delims=skip_delim))
if len(bodmas) > 1 and not bodmas[-1].strip():
bodmas[-1] = (op if op == '-' else '') + s[i + 1]
yield dm_op.join(bodmas)
skip = True
break
else:
if term:
yield term
if not skip and s[-1]:
yield s[-1]
separated = list(yield_terms(separated))
right_expr = separated.pop() if len(separated) > 1 else None
expr = op.join(separated)
if right_expr is None:
continue
return op, separated, right_expr
def _operator(self, op, left_val, right_expr, expr, local_vars, allow_recursion):
if op in ('||', '&&'):
if (op == '&&') ^ _js_ternary(left_val):
return left_val # short circuiting
elif op == '??':
if left_val not in (None, JS_Undefined):
return left_val
elif op == '?':
right_expr = _js_ternary(left_val, *self._separate(right_expr, ':', 1))
right_val = self.interpret_expression(right_expr, local_vars, allow_recursion) if right_expr else left_val
opfunc = op and next((v for k, v in self._all_operators() if k == op), None)
if not opfunc:
return right_val
try:
# print('Eval:', opfunc.__name__, left_val, right_val)
return opfunc(left_val, right_val)
except Exception as e:
raise self.Exception('Failed to evaluate {left_val!r:.50} {op} {right_val!r:.50}'.format(**locals()), expr, cause=e)
def _index(self, obj, idx, allow_undefined=None):
if idx == 'length' and isinstance(obj, list):
return len(obj)
try:
return obj[int(idx)] if isinstance(obj, list) else obj[compat_str(idx)]
except (TypeError, KeyError, IndexError, ValueError) as e:
# allow_undefined is None gives correct behaviour
if allow_undefined or (
allow_undefined is None and not isinstance(e, TypeError)):
return JS_Undefined
raise self.Exception('Cannot get index {idx!r:.100}'.format(**locals()), expr=repr(obj), cause=e)
def _dump(self, obj, namespace):
if obj is JS_Undefined:
return 'undefined'
try:
return json.dumps(obj)
except TypeError:
return self._named_object(namespace, obj)
# used below
_VAR_RET_THROW_RE = re.compile(r'''(?x)
(?:(?P<var>var|const|let)\s+|(?P<ret>return)(?:\s+|(?=["'])|$)|(?P<throw>throw)\s+)
''')
_COMPOUND_RE = re.compile(r'''(?x)
(?P<try>try)\s*\{|
(?P<if>if)\s*\(|
(?P<switch>switch)\s*\(|
(?P<for>for)\s*\(|
(?P<while>while)\s*\(
''')
_FINALLY_RE = re.compile(r'finally\s*\{')
_SWITCH_RE = re.compile(r'switch\s*\(')
def _eval_operator(self, op, left_expr, right_expr, expr, local_vars, allow_recursion):
left_val = self.interpret_expression(left_expr, local_vars, allow_recursion)
return self._operator(op, left_val, right_expr, expr, local_vars, allow_recursion)
@Debugger.wrap_interpreter
def interpret_statement(self, stmt, local_vars, allow_recursion=100):
if allow_recursion < 0:
raise self.Exception('Recursion limit reached')
allow_recursion -= 1
# print('At: ' + stmt[:60])
should_return = False
# fails on (eg) if (...) stmt1; else stmt2;
sub_statements = list(self._separate(stmt, ';')) or ['']
expr = stmt = sub_statements.pop().strip()
for sub_stmt in sub_statements:
ret, should_return = self.interpret_statement(sub_stmt, local_vars, allow_recursion)
if should_return:
return ret, should_return
m = self._VAR_RET_THROW_RE.match(stmt)
if m:
expr = stmt[len(m.group(0)):].strip()
if m.group('throw'):
raise JS_Throw(self.interpret_expression(expr, local_vars, allow_recursion))
should_return = 'return' if m.group('ret') else False
if not expr:
return None, should_return
if expr[0] in _QUOTES:
inner, outer = self._separate(expr, expr[0], 1)
if expr[0] == '/':
flags, outer = self.JS_RegExp.regex_flags(outer)
inner = self.JS_RegExp(inner[1:], flags=flags)
else:
inner = json.loads(js_to_json(inner + expr[0])) # , strict=True))
if not outer:
return inner, should_return
expr = self._named_object(local_vars, inner) + outer
new_kw, _, obj = expr.partition('new ')
if not new_kw:
for klass, konstr in (('Date', lambda *x: self.JS_Date(*x).valueOf()),
('RegExp', self.JS_RegExp),
('Error', self.Exception)):
if not obj.startswith(klass + '('):
continue
left, right = self._separate_at_paren(obj[len(klass):])
argvals = self.interpret_iter(left, local_vars, allow_recursion)
expr = konstr(*argvals)
if expr is None:
raise self.Exception('Failed to parse {klass} {left!r:.100}'.format(**locals()), expr=expr)
expr = self._dump(expr, local_vars) + right
break
else:
raise self.Exception('Unsupported object {obj:.100}'.format(**locals()), expr=expr)
# apply unary operators (see new above)
for op, _ in _UNARY_OPERATORS_X:
if not expr.startswith(op):
continue
operand = expr[len(op):]
if not operand or (op.isalpha() and operand[0] != ' '):
continue
separated = self._separate_at_op(operand, max_split=1)
if separated:
next_op, separated, right_expr = separated
separated.append(right_expr)
operand = next_op.join(separated)
return self._eval_operator(op, operand, '', expr, local_vars, allow_recursion), should_return
if expr.startswith('{'):
inner, outer = self._separate_at_paren(expr)
# try for object expression (Map)
sub_expressions = [list(self._separate(sub_expr.strip(), ':', 1)) for sub_expr in self._separate(inner)]
if all(len(sub_expr) == 2 for sub_expr in sub_expressions):
return dict(
(key_expr if re.match(_NAME_RE, key_expr) else key_expr,
self.interpret_expression(val_expr, local_vars, allow_recursion))
for key_expr, val_expr in sub_expressions), should_return
# or statement list
inner, should_abort = self.interpret_statement(inner, local_vars, allow_recursion)
if not outer or should_abort:
return inner, should_abort or should_return
else:
expr = self._dump(inner, local_vars) + outer
if expr.startswith('('):
m = re.match(r'\((?P<d>[a-z])%(?P<e>[a-z])\.length\+(?P=e)\.length\)%(?P=e)\.length', expr)
if m:
# short-cut eval of frequently used `(d%e.length+e.length)%e.length`, worth ~6% on `pytest -k test_nsig`
outer = None
inner, should_abort = self._offset_e_by_d(m.group('d'), m.group('e'), local_vars)
else:
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | true |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/__init__.py | youtube_dl/__init__.py | #!/usr/bin/env python
# coding: utf-8
from __future__ import unicode_literals
__license__ = 'Public Domain'
import io
import os
import random
import sys
from .options import (
parseOpts,
)
from .compat import (
compat_getpass,
compat_register_utf8,
compat_shlex_split,
_workaround_optparse_bug9161,
)
from .utils import (
_UnsafeExtensionError,
DateRange,
decodeOption,
DEFAULT_OUTTMPL,
DownloadError,
expand_path,
match_filter_func,
MaxDownloadsReached,
preferredencoding,
read_batch_urls,
SameFileError,
setproctitle,
std_headers,
write_string,
render_table,
)
from .update import update_self
from .downloader import (
FileDownloader,
)
from .extractor import gen_extractors, list_extractors
from .extractor.adobepass import MSO_INFO
from .YoutubeDL import YoutubeDL
def _real_main(argv=None):
# Compatibility fix for Windows
compat_register_utf8()
_workaround_optparse_bug9161()
setproctitle('youtube-dl')
parser, opts, args = parseOpts(argv)
# Set user agent
if opts.user_agent is not None:
std_headers['User-Agent'] = opts.user_agent
# Set referer
if opts.referer is not None:
std_headers['Referer'] = opts.referer
# Custom HTTP headers
if opts.headers is not None:
for h in opts.headers:
if ':' not in h:
parser.error('wrong header formatting, it should be key:value, not "%s"' % h)
key, value = h.split(':', 1)
if opts.verbose:
write_string('[debug] Adding header from command line option %s:%s\n' % (key, value))
std_headers[key] = value
# Dump user agent
if opts.dump_user_agent:
write_string(std_headers['User-Agent'] + '\n', out=sys.stdout)
sys.exit(0)
# Batch file verification
batch_urls = []
if opts.batchfile is not None:
try:
if opts.batchfile == '-':
batchfd = sys.stdin
else:
batchfd = io.open(
expand_path(opts.batchfile),
'r', encoding='utf-8', errors='ignore')
batch_urls = read_batch_urls(batchfd)
if opts.verbose:
write_string('[debug] Batch file urls: ' + repr(batch_urls) + '\n')
except IOError:
sys.exit('ERROR: batch file %s could not be read' % opts.batchfile)
all_urls = batch_urls + [url.strip() for url in args] # batch_urls are already striped in read_batch_urls
_enc = preferredencoding()
all_urls = [url.decode(_enc, 'ignore') if isinstance(url, bytes) else url for url in all_urls]
if opts.list_extractors:
for ie in list_extractors(opts.age_limit):
write_string(ie.IE_NAME + (' (CURRENTLY BROKEN)' if not ie._WORKING else '') + '\n', out=sys.stdout)
matchedUrls = [url for url in all_urls if ie.suitable(url)]
for mu in matchedUrls:
write_string(' ' + mu + '\n', out=sys.stdout)
sys.exit(0)
if opts.list_extractor_descriptions:
for ie in list_extractors(opts.age_limit):
if not ie._WORKING:
continue
desc = getattr(ie, 'IE_DESC', ie.IE_NAME)
if desc is False:
continue
if hasattr(ie, 'SEARCH_KEY'):
_SEARCHES = ('cute kittens', 'slithering pythons', 'falling cat', 'angry poodle', 'purple fish', 'running tortoise', 'sleeping bunny', 'burping cow')
_COUNTS = ('', '5', '10', 'all')
desc += ' (Example: "%s%s:%s" )' % (ie.SEARCH_KEY, random.choice(_COUNTS), random.choice(_SEARCHES))
write_string(desc + '\n', out=sys.stdout)
sys.exit(0)
if opts.ap_list_mso:
table = [[mso_id, mso_info['name']] for mso_id, mso_info in MSO_INFO.items()]
write_string('Supported TV Providers:\n' + render_table(['mso', 'mso name'], table) + '\n', out=sys.stdout)
sys.exit(0)
# Conflicting, missing and erroneous options
if opts.usenetrc and (opts.username is not None or opts.password is not None):
parser.error('using .netrc conflicts with giving username/password')
if opts.password is not None and opts.username is None:
parser.error('account username missing\n')
if opts.ap_password is not None and opts.ap_username is None:
parser.error('TV Provider account username missing\n')
if opts.outtmpl is not None and (opts.usetitle or opts.autonumber or opts.useid):
parser.error('using output template conflicts with using title, video ID or auto number')
if opts.autonumber_size is not None:
if opts.autonumber_size <= 0:
parser.error('auto number size must be positive')
if opts.autonumber_start is not None:
if opts.autonumber_start < 0:
parser.error('auto number start must be positive or 0')
if opts.usetitle and opts.useid:
parser.error('using title conflicts with using video ID')
if opts.username is not None and opts.password is None:
opts.password = compat_getpass('Type account password and press [Return]: ')
if opts.ap_username is not None and opts.ap_password is None:
opts.ap_password = compat_getpass('Type TV provider account password and press [Return]: ')
if opts.ratelimit is not None:
numeric_limit = FileDownloader.parse_bytes(opts.ratelimit)
if numeric_limit is None:
parser.error('invalid rate limit specified')
opts.ratelimit = numeric_limit
if opts.min_filesize is not None:
numeric_limit = FileDownloader.parse_bytes(opts.min_filesize)
if numeric_limit is None:
parser.error('invalid min_filesize specified')
opts.min_filesize = numeric_limit
if opts.max_filesize is not None:
numeric_limit = FileDownloader.parse_bytes(opts.max_filesize)
if numeric_limit is None:
parser.error('invalid max_filesize specified')
opts.max_filesize = numeric_limit
if opts.sleep_interval is not None:
if opts.sleep_interval < 0:
parser.error('sleep interval must be positive or 0')
if opts.max_sleep_interval is not None:
if opts.max_sleep_interval < 0:
parser.error('max sleep interval must be positive or 0')
if opts.sleep_interval is None:
parser.error('min sleep interval must be specified, use --min-sleep-interval')
if opts.max_sleep_interval < opts.sleep_interval:
parser.error('max sleep interval must be greater than or equal to min sleep interval')
else:
opts.max_sleep_interval = opts.sleep_interval
if opts.ap_mso and opts.ap_mso not in MSO_INFO:
parser.error('Unsupported TV Provider, use --ap-list-mso to get a list of supported TV Providers')
if opts.no_check_extensions:
_UnsafeExtensionError.lenient = True
def parse_retries(retries):
if retries in ('inf', 'infinite'):
parsed_retries = float('inf')
else:
try:
parsed_retries = int(retries)
except (TypeError, ValueError):
parser.error('invalid retry count specified')
return parsed_retries
if opts.retries is not None:
opts.retries = parse_retries(opts.retries)
if opts.fragment_retries is not None:
opts.fragment_retries = parse_retries(opts.fragment_retries)
if opts.buffersize is not None:
numeric_buffersize = FileDownloader.parse_bytes(opts.buffersize)
if numeric_buffersize is None:
parser.error('invalid buffer size specified')
opts.buffersize = numeric_buffersize
if opts.http_chunk_size is not None:
numeric_chunksize = FileDownloader.parse_bytes(opts.http_chunk_size)
if not numeric_chunksize:
parser.error('invalid http chunk size specified')
opts.http_chunk_size = numeric_chunksize
if opts.playliststart <= 0:
raise ValueError('Playlist start must be positive')
if opts.playlistend not in (-1, None) and opts.playlistend < opts.playliststart:
raise ValueError('Playlist end must be greater than playlist start')
if opts.extractaudio:
if opts.audioformat not in ['best', 'aac', 'flac', 'mp3', 'm4a', 'opus', 'vorbis', 'wav']:
parser.error('invalid audio format specified')
if opts.audioquality:
opts.audioquality = opts.audioquality.strip('k').strip('K')
if not opts.audioquality.isdigit():
parser.error('invalid audio quality specified')
if opts.recodevideo is not None:
if opts.recodevideo not in ['mp4', 'flv', 'webm', 'ogg', 'mkv', 'avi']:
parser.error('invalid video recode format specified')
if opts.convertsubtitles is not None:
if opts.convertsubtitles not in ['srt', 'vtt', 'ass', 'lrc']:
parser.error('invalid subtitle format specified')
if opts.date is not None:
date = DateRange.day(opts.date)
else:
date = DateRange(opts.dateafter, opts.datebefore)
# Do not download videos when there are audio-only formats
if opts.extractaudio and not opts.keepvideo and opts.format is None:
opts.format = 'bestaudio/best'
# --all-sub automatically sets --write-sub if --write-auto-sub is not given
# this was the old behaviour if only --all-sub was given.
if opts.allsubtitles and not opts.writeautomaticsub:
opts.writesubtitles = True
outtmpl = ((opts.outtmpl is not None and opts.outtmpl)
or (opts.format == '-1' and opts.usetitle and '%(title)s-%(id)s-%(format)s.%(ext)s')
or (opts.format == '-1' and '%(id)s-%(format)s.%(ext)s')
or (opts.usetitle and opts.autonumber and '%(autonumber)s-%(title)s-%(id)s.%(ext)s')
or (opts.usetitle and '%(title)s-%(id)s.%(ext)s')
or (opts.useid and '%(id)s.%(ext)s')
or (opts.autonumber and '%(autonumber)s-%(id)s.%(ext)s')
or DEFAULT_OUTTMPL)
if not os.path.splitext(outtmpl)[1] and opts.extractaudio:
parser.error('Cannot download a video and extract audio into the same'
' file! Use "{0}.%(ext)s" instead of "{0}" as the output'
' template'.format(outtmpl))
any_getting = opts.geturl or opts.gettitle or opts.getid or opts.getthumbnail or opts.getdescription or opts.getfilename or opts.getformat or opts.getduration or opts.dumpjson or opts.dump_single_json
any_printing = opts.print_json
download_archive_fn = expand_path(opts.download_archive) if opts.download_archive is not None else opts.download_archive
# PostProcessors
postprocessors = []
if opts.metafromtitle:
postprocessors.append({
'key': 'MetadataFromTitle',
'titleformat': opts.metafromtitle
})
if opts.extractaudio:
postprocessors.append({
'key': 'FFmpegExtractAudio',
'preferredcodec': opts.audioformat,
'preferredquality': opts.audioquality,
'nopostoverwrites': opts.nopostoverwrites,
})
if opts.recodevideo:
postprocessors.append({
'key': 'FFmpegVideoConvertor',
'preferedformat': opts.recodevideo,
})
# FFmpegMetadataPP should be run after FFmpegVideoConvertorPP and
# FFmpegExtractAudioPP as containers before conversion may not support
# metadata (3gp, webm, etc.)
# And this post-processor should be placed before other metadata
# manipulating post-processors (FFmpegEmbedSubtitle) to prevent loss of
# extra metadata. By default ffmpeg preserves metadata applicable for both
# source and target containers. From this point the container won't change,
# so metadata can be added here.
if opts.addmetadata:
postprocessors.append({'key': 'FFmpegMetadata'})
if opts.convertsubtitles:
postprocessors.append({
'key': 'FFmpegSubtitlesConvertor',
'format': opts.convertsubtitles,
})
if opts.embedsubtitles:
postprocessors.append({
'key': 'FFmpegEmbedSubtitle',
})
if opts.embedthumbnail:
already_have_thumbnail = opts.writethumbnail or opts.write_all_thumbnails
postprocessors.append({
'key': 'EmbedThumbnail',
'already_have_thumbnail': already_have_thumbnail
})
if not already_have_thumbnail:
opts.writethumbnail = True
# XAttrMetadataPP should be run after post-processors that may change file
# contents
if opts.xattrs:
postprocessors.append({'key': 'XAttrMetadata'})
# Please keep ExecAfterDownload towards the bottom as it allows the user to modify the final file in any way.
# So if the user is able to remove the file before your postprocessor runs it might cause a few problems.
if opts.exec_cmd:
postprocessors.append({
'key': 'ExecAfterDownload',
'exec_cmd': opts.exec_cmd,
})
external_downloader_args = None
if opts.external_downloader_args:
external_downloader_args = compat_shlex_split(opts.external_downloader_args)
postprocessor_args = None
if opts.postprocessor_args:
postprocessor_args = compat_shlex_split(opts.postprocessor_args)
match_filter = (
None if opts.match_filter is None
else match_filter_func(opts.match_filter))
ydl_opts = {
'usenetrc': opts.usenetrc,
'username': opts.username,
'password': opts.password,
'twofactor': opts.twofactor,
'videopassword': opts.videopassword,
'ap_mso': opts.ap_mso,
'ap_username': opts.ap_username,
'ap_password': opts.ap_password,
'quiet': (opts.quiet or any_getting or any_printing),
'no_warnings': opts.no_warnings,
'forceurl': opts.geturl,
'forcetitle': opts.gettitle,
'forceid': opts.getid,
'forcethumbnail': opts.getthumbnail,
'forcedescription': opts.getdescription,
'forceduration': opts.getduration,
'forcefilename': opts.getfilename,
'forceformat': opts.getformat,
'forcejson': opts.dumpjson or opts.print_json,
'dump_single_json': opts.dump_single_json,
'simulate': opts.simulate or any_getting,
'skip_download': opts.skip_download,
'format': opts.format,
'listformats': opts.listformats,
'outtmpl': outtmpl,
'outtmpl_na_placeholder': opts.outtmpl_na_placeholder,
'autonumber_size': opts.autonumber_size,
'autonumber_start': opts.autonumber_start,
'restrictfilenames': opts.restrictfilenames,
'ignoreerrors': opts.ignoreerrors,
'force_generic_extractor': opts.force_generic_extractor,
'ratelimit': opts.ratelimit,
'nooverwrites': opts.nooverwrites,
'retries': opts.retries,
'fragment_retries': opts.fragment_retries,
'skip_unavailable_fragments': opts.skip_unavailable_fragments,
'keep_fragments': opts.keep_fragments,
'buffersize': opts.buffersize,
'noresizebuffer': opts.noresizebuffer,
'http_chunk_size': opts.http_chunk_size,
'continuedl': opts.continue_dl,
'noprogress': opts.noprogress,
'progress_with_newline': opts.progress_with_newline,
'playliststart': opts.playliststart,
'playlistend': opts.playlistend,
'playlistreverse': opts.playlist_reverse,
'playlistrandom': opts.playlist_random,
'noplaylist': opts.noplaylist,
'logtostderr': opts.outtmpl == '-',
'consoletitle': opts.consoletitle,
'nopart': opts.nopart,
'updatetime': opts.updatetime,
'writedescription': opts.writedescription,
'writeannotations': opts.writeannotations,
'writeinfojson': opts.writeinfojson,
'writethumbnail': opts.writethumbnail,
'write_all_thumbnails': opts.write_all_thumbnails,
'writesubtitles': opts.writesubtitles,
'writeautomaticsub': opts.writeautomaticsub,
'allsubtitles': opts.allsubtitles,
'listsubtitles': opts.listsubtitles,
'subtitlesformat': opts.subtitlesformat,
'subtitleslangs': opts.subtitleslangs,
'matchtitle': decodeOption(opts.matchtitle),
'rejecttitle': decodeOption(opts.rejecttitle),
'max_downloads': opts.max_downloads,
'prefer_free_formats': opts.prefer_free_formats,
'verbose': opts.verbose,
'dump_intermediate_pages': opts.dump_intermediate_pages,
'write_pages': opts.write_pages,
'test': opts.test,
'keepvideo': opts.keepvideo,
'min_filesize': opts.min_filesize,
'max_filesize': opts.max_filesize,
'min_views': opts.min_views,
'max_views': opts.max_views,
'daterange': date,
'cachedir': opts.cachedir,
'youtube_print_sig_code': opts.youtube_print_sig_code,
'age_limit': opts.age_limit,
'download_archive': download_archive_fn,
'cookiefile': opts.cookiefile,
'nocheckcertificate': opts.no_check_certificate,
'prefer_insecure': opts.prefer_insecure,
'proxy': opts.proxy,
'socket_timeout': opts.socket_timeout,
'bidi_workaround': opts.bidi_workaround,
'debug_printtraffic': opts.debug_printtraffic,
'prefer_ffmpeg': opts.prefer_ffmpeg,
'include_ads': opts.include_ads,
'default_search': opts.default_search,
'youtube_include_dash_manifest': opts.youtube_include_dash_manifest,
'youtube_player_js_version': opts.youtube_player_js_version,
'youtube_player_js_variant': opts.youtube_player_js_variant,
'encoding': opts.encoding,
'extract_flat': opts.extract_flat,
'mark_watched': opts.mark_watched,
'merge_output_format': opts.merge_output_format,
'postprocessors': postprocessors,
'fixup': opts.fixup,
'source_address': opts.source_address,
'call_home': opts.call_home,
'sleep_interval': opts.sleep_interval,
'max_sleep_interval': opts.max_sleep_interval,
'external_downloader': opts.external_downloader,
'list_thumbnails': opts.list_thumbnails,
'playlist_items': opts.playlist_items,
'xattr_set_filesize': opts.xattr_set_filesize,
'match_filter': match_filter,
'no_color': opts.no_color,
'ffmpeg_location': opts.ffmpeg_location,
'hls_prefer_native': opts.hls_prefer_native,
'hls_use_mpegts': opts.hls_use_mpegts,
'external_downloader_args': external_downloader_args,
'postprocessor_args': postprocessor_args,
'cn_verification_proxy': opts.cn_verification_proxy,
'geo_verification_proxy': opts.geo_verification_proxy,
'config_location': opts.config_location,
'geo_bypass': opts.geo_bypass,
'geo_bypass_country': opts.geo_bypass_country,
'geo_bypass_ip_block': opts.geo_bypass_ip_block,
# just for deprecation check
'autonumber': opts.autonumber if opts.autonumber is True else None,
'usetitle': opts.usetitle if opts.usetitle is True else None,
}
with YoutubeDL(ydl_opts) as ydl:
# Update version
if opts.update_self:
update_self(ydl.to_screen, opts.verbose, ydl._opener)
# Remove cache dir
if opts.rm_cachedir:
ydl.cache.remove()
# Maybe do nothing
if (len(all_urls) < 1) and (opts.load_info_filename is None):
if opts.update_self or opts.rm_cachedir:
sys.exit()
ydl.warn_if_short_id(sys.argv[1:] if argv is None else argv)
parser.error(
'You must provide at least one URL.\n'
'Type youtube-dl --help to see a list of all options.')
try:
if opts.load_info_filename is not None:
retcode = ydl.download_with_info_file(expand_path(opts.load_info_filename))
else:
retcode = ydl.download(all_urls)
except MaxDownloadsReached:
ydl.to_screen('--max-download limit reached, aborting.')
retcode = 101
sys.exit(retcode)
def main(argv=None):
try:
_real_main(argv)
except DownloadError:
sys.exit(1)
except SameFileError:
sys.exit('ERROR: fixed output name but more than one file to download')
except KeyboardInterrupt:
sys.exit('\nERROR: Interrupted by user')
__all__ = ['main', 'YoutubeDL', 'gen_extractors', 'list_extractors']
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/aes.py | youtube_dl/aes.py | from __future__ import unicode_literals
from math import ceil
from .compat import compat_b64decode
from .utils import bytes_to_intlist, intlist_to_bytes
BLOCK_SIZE_BYTES = 16
def pkcs7_padding(data):
"""
PKCS#7 padding
@param {int[]} data cleartext
@returns {int[]} padding data
"""
remaining_length = BLOCK_SIZE_BYTES - len(data) % BLOCK_SIZE_BYTES
return data + [remaining_length] * remaining_length
def aes_ctr_decrypt(data, key, counter):
"""
Decrypt with aes in counter mode
@param {int[]} data cipher
@param {int[]} key 16/24/32-Byte cipher key
@param {instance} counter Instance whose next_value function (@returns {int[]} 16-Byte block)
returns the next counter block
@returns {int[]} decrypted data
"""
expanded_key = key_expansion(key)
block_count = int(ceil(float(len(data)) / BLOCK_SIZE_BYTES))
decrypted_data = []
for i in range(block_count):
counter_block = counter.next_value()
block = data[i * BLOCK_SIZE_BYTES: (i + 1) * BLOCK_SIZE_BYTES]
block += [0] * (BLOCK_SIZE_BYTES - len(block))
cipher_counter_block = aes_encrypt(counter_block, expanded_key)
decrypted_data += xor(block, cipher_counter_block)
decrypted_data = decrypted_data[:len(data)]
return decrypted_data
def aes_cbc_decrypt(data, key, iv):
"""
Decrypt with aes in CBC mode
@param {int[]} data cipher
@param {int[]} key 16/24/32-Byte cipher key
@param {int[]} iv 16-Byte IV
@returns {int[]} decrypted data
"""
expanded_key = key_expansion(key)
block_count = int(ceil(float(len(data)) / BLOCK_SIZE_BYTES))
decrypted_data = []
previous_cipher_block = iv
for i in range(block_count):
block = data[i * BLOCK_SIZE_BYTES: (i + 1) * BLOCK_SIZE_BYTES]
block += [0] * (BLOCK_SIZE_BYTES - len(block))
decrypted_block = aes_decrypt(block, expanded_key)
decrypted_data += xor(decrypted_block, previous_cipher_block)
previous_cipher_block = block
decrypted_data = decrypted_data[:len(data)]
return decrypted_data
def aes_cbc_encrypt(data, key, iv):
"""
Encrypt with aes in CBC mode. Using PKCS#7 padding
@param {int[]} data cleartext
@param {int[]} key 16/24/32-Byte cipher key
@param {int[]} iv 16-Byte IV
@returns {int[]} encrypted data
"""
expanded_key = key_expansion(key)
block_count = int(ceil(float(len(data)) / BLOCK_SIZE_BYTES))
encrypted_data = []
previous_cipher_block = iv
for i in range(block_count):
block = data[i * BLOCK_SIZE_BYTES: (i + 1) * BLOCK_SIZE_BYTES]
block = pkcs7_padding(block)
mixed_block = xor(block, previous_cipher_block)
encrypted_block = aes_encrypt(mixed_block, expanded_key)
encrypted_data += encrypted_block
previous_cipher_block = encrypted_block
return encrypted_data
def aes_ecb_encrypt(data, key):
"""
Encrypt with aes in ECB mode. Using PKCS#7 padding
@param {int[]} data cleartext
@param {int[]} key 16/24/32-Byte cipher key
@returns {int[]} encrypted data
"""
expanded_key = key_expansion(key)
block_count = int(ceil(float(len(data)) / BLOCK_SIZE_BYTES))
encrypted_data = []
for i in range(block_count):
block = data[i * BLOCK_SIZE_BYTES: (i + 1) * BLOCK_SIZE_BYTES]
block = pkcs7_padding(block)
encrypted_block = aes_encrypt(block, expanded_key)
encrypted_data += encrypted_block
return encrypted_data
def key_expansion(data):
"""
Generate key schedule
@param {int[]} data 16/24/32-Byte cipher key
@returns {int[]} 176/208/240-Byte expanded key
"""
data = data[:] # copy
rcon_iteration = 1
key_size_bytes = len(data)
expanded_key_size_bytes = (key_size_bytes // 4 + 7) * BLOCK_SIZE_BYTES
while len(data) < expanded_key_size_bytes:
temp = data[-4:]
temp = key_schedule_core(temp, rcon_iteration)
rcon_iteration += 1
data += xor(temp, data[-key_size_bytes: 4 - key_size_bytes])
for _ in range(3):
temp = data[-4:]
data += xor(temp, data[-key_size_bytes: 4 - key_size_bytes])
if key_size_bytes == 32:
temp = data[-4:]
temp = sub_bytes(temp)
data += xor(temp, data[-key_size_bytes: 4 - key_size_bytes])
for _ in range(3 if key_size_bytes == 32 else 2 if key_size_bytes == 24 else 0):
temp = data[-4:]
data += xor(temp, data[-key_size_bytes: 4 - key_size_bytes])
data = data[:expanded_key_size_bytes]
return data
def aes_encrypt(data, expanded_key):
"""
Encrypt one block with aes
@param {int[]} data 16-Byte state
@param {int[]} expanded_key 176/208/240-Byte expanded key
@returns {int[]} 16-Byte cipher
"""
rounds = len(expanded_key) // BLOCK_SIZE_BYTES - 1
data = xor(data, expanded_key[:BLOCK_SIZE_BYTES])
for i in range(1, rounds + 1):
data = sub_bytes(data)
data = shift_rows(data)
if i != rounds:
data = mix_columns(data)
data = xor(data, expanded_key[i * BLOCK_SIZE_BYTES: (i + 1) * BLOCK_SIZE_BYTES])
return data
def aes_decrypt(data, expanded_key):
"""
Decrypt one block with aes
@param {int[]} data 16-Byte cipher
@param {int[]} expanded_key 176/208/240-Byte expanded key
@returns {int[]} 16-Byte state
"""
rounds = len(expanded_key) // BLOCK_SIZE_BYTES - 1
for i in range(rounds, 0, -1):
data = xor(data, expanded_key[i * BLOCK_SIZE_BYTES: (i + 1) * BLOCK_SIZE_BYTES])
if i != rounds:
data = mix_columns_inv(data)
data = shift_rows_inv(data)
data = sub_bytes_inv(data)
data = xor(data, expanded_key[:BLOCK_SIZE_BYTES])
return data
def aes_decrypt_text(data, password, key_size_bytes):
"""
Decrypt text
- The first 8 Bytes of decoded 'data' are the 8 high Bytes of the counter
- The cipher key is retrieved by encrypting the first 16 Byte of 'password'
with the first 'key_size_bytes' Bytes from 'password' (if necessary filled with 0's)
- Mode of operation is 'counter'
@param {str} data Base64 encoded string
@param {str,unicode} password Password (will be encoded with utf-8)
@param {int} key_size_bytes Possible values: 16 for 128-Bit, 24 for 192-Bit or 32 for 256-Bit
@returns {str} Decrypted data
"""
NONCE_LENGTH_BYTES = 8
data = bytes_to_intlist(compat_b64decode(data))
password = bytes_to_intlist(password.encode('utf-8'))
key = password[:key_size_bytes] + [0] * (key_size_bytes - len(password))
key = aes_encrypt(key[:BLOCK_SIZE_BYTES], key_expansion(key)) * (key_size_bytes // BLOCK_SIZE_BYTES)
nonce = data[:NONCE_LENGTH_BYTES]
cipher = data[NONCE_LENGTH_BYTES:]
class Counter(object):
__value = nonce + [0] * (BLOCK_SIZE_BYTES - NONCE_LENGTH_BYTES)
def next_value(self):
temp = self.__value
self.__value = inc(self.__value)
return temp
decrypted_data = aes_ctr_decrypt(cipher, key, Counter())
plaintext = intlist_to_bytes(decrypted_data)
return plaintext
RCON = (0x8d, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x1b, 0x36)
SBOX = (0x63, 0x7C, 0x77, 0x7B, 0xF2, 0x6B, 0x6F, 0xC5, 0x30, 0x01, 0x67, 0x2B, 0xFE, 0xD7, 0xAB, 0x76,
0xCA, 0x82, 0xC9, 0x7D, 0xFA, 0x59, 0x47, 0xF0, 0xAD, 0xD4, 0xA2, 0xAF, 0x9C, 0xA4, 0x72, 0xC0,
0xB7, 0xFD, 0x93, 0x26, 0x36, 0x3F, 0xF7, 0xCC, 0x34, 0xA5, 0xE5, 0xF1, 0x71, 0xD8, 0x31, 0x15,
0x04, 0xC7, 0x23, 0xC3, 0x18, 0x96, 0x05, 0x9A, 0x07, 0x12, 0x80, 0xE2, 0xEB, 0x27, 0xB2, 0x75,
0x09, 0x83, 0x2C, 0x1A, 0x1B, 0x6E, 0x5A, 0xA0, 0x52, 0x3B, 0xD6, 0xB3, 0x29, 0xE3, 0x2F, 0x84,
0x53, 0xD1, 0x00, 0xED, 0x20, 0xFC, 0xB1, 0x5B, 0x6A, 0xCB, 0xBE, 0x39, 0x4A, 0x4C, 0x58, 0xCF,
0xD0, 0xEF, 0xAA, 0xFB, 0x43, 0x4D, 0x33, 0x85, 0x45, 0xF9, 0x02, 0x7F, 0x50, 0x3C, 0x9F, 0xA8,
0x51, 0xA3, 0x40, 0x8F, 0x92, 0x9D, 0x38, 0xF5, 0xBC, 0xB6, 0xDA, 0x21, 0x10, 0xFF, 0xF3, 0xD2,
0xCD, 0x0C, 0x13, 0xEC, 0x5F, 0x97, 0x44, 0x17, 0xC4, 0xA7, 0x7E, 0x3D, 0x64, 0x5D, 0x19, 0x73,
0x60, 0x81, 0x4F, 0xDC, 0x22, 0x2A, 0x90, 0x88, 0x46, 0xEE, 0xB8, 0x14, 0xDE, 0x5E, 0x0B, 0xDB,
0xE0, 0x32, 0x3A, 0x0A, 0x49, 0x06, 0x24, 0x5C, 0xC2, 0xD3, 0xAC, 0x62, 0x91, 0x95, 0xE4, 0x79,
0xE7, 0xC8, 0x37, 0x6D, 0x8D, 0xD5, 0x4E, 0xA9, 0x6C, 0x56, 0xF4, 0xEA, 0x65, 0x7A, 0xAE, 0x08,
0xBA, 0x78, 0x25, 0x2E, 0x1C, 0xA6, 0xB4, 0xC6, 0xE8, 0xDD, 0x74, 0x1F, 0x4B, 0xBD, 0x8B, 0x8A,
0x70, 0x3E, 0xB5, 0x66, 0x48, 0x03, 0xF6, 0x0E, 0x61, 0x35, 0x57, 0xB9, 0x86, 0xC1, 0x1D, 0x9E,
0xE1, 0xF8, 0x98, 0x11, 0x69, 0xD9, 0x8E, 0x94, 0x9B, 0x1E, 0x87, 0xE9, 0xCE, 0x55, 0x28, 0xDF,
0x8C, 0xA1, 0x89, 0x0D, 0xBF, 0xE6, 0x42, 0x68, 0x41, 0x99, 0x2D, 0x0F, 0xB0, 0x54, 0xBB, 0x16)
SBOX_INV = (0x52, 0x09, 0x6a, 0xd5, 0x30, 0x36, 0xa5, 0x38, 0xbf, 0x40, 0xa3, 0x9e, 0x81, 0xf3, 0xd7, 0xfb,
0x7c, 0xe3, 0x39, 0x82, 0x9b, 0x2f, 0xff, 0x87, 0x34, 0x8e, 0x43, 0x44, 0xc4, 0xde, 0xe9, 0xcb,
0x54, 0x7b, 0x94, 0x32, 0xa6, 0xc2, 0x23, 0x3d, 0xee, 0x4c, 0x95, 0x0b, 0x42, 0xfa, 0xc3, 0x4e,
0x08, 0x2e, 0xa1, 0x66, 0x28, 0xd9, 0x24, 0xb2, 0x76, 0x5b, 0xa2, 0x49, 0x6d, 0x8b, 0xd1, 0x25,
0x72, 0xf8, 0xf6, 0x64, 0x86, 0x68, 0x98, 0x16, 0xd4, 0xa4, 0x5c, 0xcc, 0x5d, 0x65, 0xb6, 0x92,
0x6c, 0x70, 0x48, 0x50, 0xfd, 0xed, 0xb9, 0xda, 0x5e, 0x15, 0x46, 0x57, 0xa7, 0x8d, 0x9d, 0x84,
0x90, 0xd8, 0xab, 0x00, 0x8c, 0xbc, 0xd3, 0x0a, 0xf7, 0xe4, 0x58, 0x05, 0xb8, 0xb3, 0x45, 0x06,
0xd0, 0x2c, 0x1e, 0x8f, 0xca, 0x3f, 0x0f, 0x02, 0xc1, 0xaf, 0xbd, 0x03, 0x01, 0x13, 0x8a, 0x6b,
0x3a, 0x91, 0x11, 0x41, 0x4f, 0x67, 0xdc, 0xea, 0x97, 0xf2, 0xcf, 0xce, 0xf0, 0xb4, 0xe6, 0x73,
0x96, 0xac, 0x74, 0x22, 0xe7, 0xad, 0x35, 0x85, 0xe2, 0xf9, 0x37, 0xe8, 0x1c, 0x75, 0xdf, 0x6e,
0x47, 0xf1, 0x1a, 0x71, 0x1d, 0x29, 0xc5, 0x89, 0x6f, 0xb7, 0x62, 0x0e, 0xaa, 0x18, 0xbe, 0x1b,
0xfc, 0x56, 0x3e, 0x4b, 0xc6, 0xd2, 0x79, 0x20, 0x9a, 0xdb, 0xc0, 0xfe, 0x78, 0xcd, 0x5a, 0xf4,
0x1f, 0xdd, 0xa8, 0x33, 0x88, 0x07, 0xc7, 0x31, 0xb1, 0x12, 0x10, 0x59, 0x27, 0x80, 0xec, 0x5f,
0x60, 0x51, 0x7f, 0xa9, 0x19, 0xb5, 0x4a, 0x0d, 0x2d, 0xe5, 0x7a, 0x9f, 0x93, 0xc9, 0x9c, 0xef,
0xa0, 0xe0, 0x3b, 0x4d, 0xae, 0x2a, 0xf5, 0xb0, 0xc8, 0xeb, 0xbb, 0x3c, 0x83, 0x53, 0x99, 0x61,
0x17, 0x2b, 0x04, 0x7e, 0xba, 0x77, 0xd6, 0x26, 0xe1, 0x69, 0x14, 0x63, 0x55, 0x21, 0x0c, 0x7d)
MIX_COLUMN_MATRIX = ((0x2, 0x3, 0x1, 0x1),
(0x1, 0x2, 0x3, 0x1),
(0x1, 0x1, 0x2, 0x3),
(0x3, 0x1, 0x1, 0x2))
MIX_COLUMN_MATRIX_INV = ((0xE, 0xB, 0xD, 0x9),
(0x9, 0xE, 0xB, 0xD),
(0xD, 0x9, 0xE, 0xB),
(0xB, 0xD, 0x9, 0xE))
RIJNDAEL_EXP_TABLE = (0x01, 0x03, 0x05, 0x0F, 0x11, 0x33, 0x55, 0xFF, 0x1A, 0x2E, 0x72, 0x96, 0xA1, 0xF8, 0x13, 0x35,
0x5F, 0xE1, 0x38, 0x48, 0xD8, 0x73, 0x95, 0xA4, 0xF7, 0x02, 0x06, 0x0A, 0x1E, 0x22, 0x66, 0xAA,
0xE5, 0x34, 0x5C, 0xE4, 0x37, 0x59, 0xEB, 0x26, 0x6A, 0xBE, 0xD9, 0x70, 0x90, 0xAB, 0xE6, 0x31,
0x53, 0xF5, 0x04, 0x0C, 0x14, 0x3C, 0x44, 0xCC, 0x4F, 0xD1, 0x68, 0xB8, 0xD3, 0x6E, 0xB2, 0xCD,
0x4C, 0xD4, 0x67, 0xA9, 0xE0, 0x3B, 0x4D, 0xD7, 0x62, 0xA6, 0xF1, 0x08, 0x18, 0x28, 0x78, 0x88,
0x83, 0x9E, 0xB9, 0xD0, 0x6B, 0xBD, 0xDC, 0x7F, 0x81, 0x98, 0xB3, 0xCE, 0x49, 0xDB, 0x76, 0x9A,
0xB5, 0xC4, 0x57, 0xF9, 0x10, 0x30, 0x50, 0xF0, 0x0B, 0x1D, 0x27, 0x69, 0xBB, 0xD6, 0x61, 0xA3,
0xFE, 0x19, 0x2B, 0x7D, 0x87, 0x92, 0xAD, 0xEC, 0x2F, 0x71, 0x93, 0xAE, 0xE9, 0x20, 0x60, 0xA0,
0xFB, 0x16, 0x3A, 0x4E, 0xD2, 0x6D, 0xB7, 0xC2, 0x5D, 0xE7, 0x32, 0x56, 0xFA, 0x15, 0x3F, 0x41,
0xC3, 0x5E, 0xE2, 0x3D, 0x47, 0xC9, 0x40, 0xC0, 0x5B, 0xED, 0x2C, 0x74, 0x9C, 0xBF, 0xDA, 0x75,
0x9F, 0xBA, 0xD5, 0x64, 0xAC, 0xEF, 0x2A, 0x7E, 0x82, 0x9D, 0xBC, 0xDF, 0x7A, 0x8E, 0x89, 0x80,
0x9B, 0xB6, 0xC1, 0x58, 0xE8, 0x23, 0x65, 0xAF, 0xEA, 0x25, 0x6F, 0xB1, 0xC8, 0x43, 0xC5, 0x54,
0xFC, 0x1F, 0x21, 0x63, 0xA5, 0xF4, 0x07, 0x09, 0x1B, 0x2D, 0x77, 0x99, 0xB0, 0xCB, 0x46, 0xCA,
0x45, 0xCF, 0x4A, 0xDE, 0x79, 0x8B, 0x86, 0x91, 0xA8, 0xE3, 0x3E, 0x42, 0xC6, 0x51, 0xF3, 0x0E,
0x12, 0x36, 0x5A, 0xEE, 0x29, 0x7B, 0x8D, 0x8C, 0x8F, 0x8A, 0x85, 0x94, 0xA7, 0xF2, 0x0D, 0x17,
0x39, 0x4B, 0xDD, 0x7C, 0x84, 0x97, 0xA2, 0xFD, 0x1C, 0x24, 0x6C, 0xB4, 0xC7, 0x52, 0xF6, 0x01)
RIJNDAEL_LOG_TABLE = (0x00, 0x00, 0x19, 0x01, 0x32, 0x02, 0x1a, 0xc6, 0x4b, 0xc7, 0x1b, 0x68, 0x33, 0xee, 0xdf, 0x03,
0x64, 0x04, 0xe0, 0x0e, 0x34, 0x8d, 0x81, 0xef, 0x4c, 0x71, 0x08, 0xc8, 0xf8, 0x69, 0x1c, 0xc1,
0x7d, 0xc2, 0x1d, 0xb5, 0xf9, 0xb9, 0x27, 0x6a, 0x4d, 0xe4, 0xa6, 0x72, 0x9a, 0xc9, 0x09, 0x78,
0x65, 0x2f, 0x8a, 0x05, 0x21, 0x0f, 0xe1, 0x24, 0x12, 0xf0, 0x82, 0x45, 0x35, 0x93, 0xda, 0x8e,
0x96, 0x8f, 0xdb, 0xbd, 0x36, 0xd0, 0xce, 0x94, 0x13, 0x5c, 0xd2, 0xf1, 0x40, 0x46, 0x83, 0x38,
0x66, 0xdd, 0xfd, 0x30, 0xbf, 0x06, 0x8b, 0x62, 0xb3, 0x25, 0xe2, 0x98, 0x22, 0x88, 0x91, 0x10,
0x7e, 0x6e, 0x48, 0xc3, 0xa3, 0xb6, 0x1e, 0x42, 0x3a, 0x6b, 0x28, 0x54, 0xfa, 0x85, 0x3d, 0xba,
0x2b, 0x79, 0x0a, 0x15, 0x9b, 0x9f, 0x5e, 0xca, 0x4e, 0xd4, 0xac, 0xe5, 0xf3, 0x73, 0xa7, 0x57,
0xaf, 0x58, 0xa8, 0x50, 0xf4, 0xea, 0xd6, 0x74, 0x4f, 0xae, 0xe9, 0xd5, 0xe7, 0xe6, 0xad, 0xe8,
0x2c, 0xd7, 0x75, 0x7a, 0xeb, 0x16, 0x0b, 0xf5, 0x59, 0xcb, 0x5f, 0xb0, 0x9c, 0xa9, 0x51, 0xa0,
0x7f, 0x0c, 0xf6, 0x6f, 0x17, 0xc4, 0x49, 0xec, 0xd8, 0x43, 0x1f, 0x2d, 0xa4, 0x76, 0x7b, 0xb7,
0xcc, 0xbb, 0x3e, 0x5a, 0xfb, 0x60, 0xb1, 0x86, 0x3b, 0x52, 0xa1, 0x6c, 0xaa, 0x55, 0x29, 0x9d,
0x97, 0xb2, 0x87, 0x90, 0x61, 0xbe, 0xdc, 0xfc, 0xbc, 0x95, 0xcf, 0xcd, 0x37, 0x3f, 0x5b, 0xd1,
0x53, 0x39, 0x84, 0x3c, 0x41, 0xa2, 0x6d, 0x47, 0x14, 0x2a, 0x9e, 0x5d, 0x56, 0xf2, 0xd3, 0xab,
0x44, 0x11, 0x92, 0xd9, 0x23, 0x20, 0x2e, 0x89, 0xb4, 0x7c, 0xb8, 0x26, 0x77, 0x99, 0xe3, 0xa5,
0x67, 0x4a, 0xed, 0xde, 0xc5, 0x31, 0xfe, 0x18, 0x0d, 0x63, 0x8c, 0x80, 0xc0, 0xf7, 0x70, 0x07)
def sub_bytes(data):
return [SBOX[x] for x in data]
def sub_bytes_inv(data):
return [SBOX_INV[x] for x in data]
def rotate(data):
return data[1:] + [data[0]]
def key_schedule_core(data, rcon_iteration):
data = rotate(data)
data = sub_bytes(data)
data[0] = data[0] ^ RCON[rcon_iteration]
return data
def xor(data1, data2):
return [x ^ y for x, y in zip(data1, data2)]
def rijndael_mul(a, b):
if (a == 0 or b == 0):
return 0
return RIJNDAEL_EXP_TABLE[(RIJNDAEL_LOG_TABLE[a] + RIJNDAEL_LOG_TABLE[b]) % 0xFF]
def mix_column(data, matrix):
data_mixed = []
for row in range(4):
mixed = 0
for column in range(4):
# xor is (+) and (-)
mixed ^= rijndael_mul(data[column], matrix[row][column])
data_mixed.append(mixed)
return data_mixed
def mix_columns(data, matrix=MIX_COLUMN_MATRIX):
data_mixed = []
for i in range(4):
column = data[i * 4: (i + 1) * 4]
data_mixed += mix_column(column, matrix)
return data_mixed
def mix_columns_inv(data):
return mix_columns(data, MIX_COLUMN_MATRIX_INV)
def shift_rows(data):
data_shifted = []
for column in range(4):
for row in range(4):
data_shifted.append(data[((column + row) & 0b11) * 4 + row])
return data_shifted
def shift_rows_inv(data):
data_shifted = []
for column in range(4):
for row in range(4):
data_shifted.append(data[((column - row) & 0b11) * 4 + row])
return data_shifted
def inc(data):
data = data[:] # copy
for i in range(len(data) - 1, -1, -1):
if data[i] == 255:
data[i] = 0
else:
data[i] = data[i] + 1
break
return data
__all__ = ['aes_encrypt', 'key_expansion', 'aes_ctr_decrypt', 'aes_cbc_decrypt', 'aes_decrypt_text']
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/casefold.py | youtube_dl/casefold.py | # coding: utf-8
from __future__ import unicode_literals
from .compat import (
compat_str,
compat_chr,
)
# Below is included the text of icu/CaseFolding.txt retrieved from
# https://github.com/unicode-org/icu/blob/main/icu4c/source/data/unidata/CaseFolding.txt
# In case newly foldable Unicode characters are defined, paste the new version
# of the text inside the ''' marks.
# The text is expected to have only blank lines and lines with 1st character #,
# all ignored, and fold definitions like this:
# `from_hex_code; status; space_separated_to_hex_code_list; comment`
# Only `status` C/F are used.
_map_str = '''
# CaseFolding-15.0.0.txt
# Date: 2022-02-02, 23:35:35 GMT
# © 2022 Unicode®, Inc.
# Unicode and the Unicode Logo are registered trademarks of Unicode, Inc. in the U.S. and other countries.
# For terms of use, see https://www.unicode.org/terms_of_use.html
#
# Unicode Character Database
# For documentation, see https://www.unicode.org/reports/tr44/
#
# Case Folding Properties
#
# This file is a supplement to the UnicodeData file.
# It provides a case folding mapping generated from the Unicode Character Database.
# If all characters are mapped according to the full mapping below, then
# case differences (according to UnicodeData.txt and SpecialCasing.txt)
# are eliminated.
#
# The data supports both implementations that require simple case foldings
# (where string lengths don't change), and implementations that allow full case folding
# (where string lengths may grow). Note that where they can be supported, the
# full case foldings are superior: for example, they allow "MASSE" and "Maße" to match.
#
# All code points not listed in this file map to themselves.
#
# NOTE: case folding does not preserve normalization formats!
#
# For information on case folding, including how to have case folding
# preserve normalization formats, see Section 3.13 Default Case Algorithms in
# The Unicode Standard.
#
# ================================================================================
# Format
# ================================================================================
# The entries in this file are in the following machine-readable format:
#
# <code>; <status>; <mapping>; # <name>
#
# The status field is:
# C: common case folding, common mappings shared by both simple and full mappings.
# F: full case folding, mappings that cause strings to grow in length. Multiple characters are separated by spaces.
# S: simple case folding, mappings to single characters where different from F.
# T: special case for uppercase I and dotted uppercase I
# - For non-Turkic languages, this mapping is normally not used.
# - For Turkic languages (tr, az), this mapping can be used instead of the normal mapping for these characters.
# Note that the Turkic mappings do not maintain canonical equivalence without additional processing.
# See the discussions of case mapping in the Unicode Standard for more information.
#
# Usage:
# A. To do a simple case folding, use the mappings with status C + S.
# B. To do a full case folding, use the mappings with status C + F.
#
# The mappings with status T can be used or omitted depending on the desired case-folding
# behavior. (The default option is to exclude them.)
#
# =================================================================
# Property: Case_Folding
# All code points not explicitly listed for Case_Folding
# have the value C for the status field, and the code point itself for the mapping field.
# =================================================================
0041; C; 0061; # LATIN CAPITAL LETTER A
0042; C; 0062; # LATIN CAPITAL LETTER B
0043; C; 0063; # LATIN CAPITAL LETTER C
0044; C; 0064; # LATIN CAPITAL LETTER D
0045; C; 0065; # LATIN CAPITAL LETTER E
0046; C; 0066; # LATIN CAPITAL LETTER F
0047; C; 0067; # LATIN CAPITAL LETTER G
0048; C; 0068; # LATIN CAPITAL LETTER H
0049; C; 0069; # LATIN CAPITAL LETTER I
0049; T; 0131; # LATIN CAPITAL LETTER I
004A; C; 006A; # LATIN CAPITAL LETTER J
004B; C; 006B; # LATIN CAPITAL LETTER K
004C; C; 006C; # LATIN CAPITAL LETTER L
004D; C; 006D; # LATIN CAPITAL LETTER M
004E; C; 006E; # LATIN CAPITAL LETTER N
004F; C; 006F; # LATIN CAPITAL LETTER O
0050; C; 0070; # LATIN CAPITAL LETTER P
0051; C; 0071; # LATIN CAPITAL LETTER Q
0052; C; 0072; # LATIN CAPITAL LETTER R
0053; C; 0073; # LATIN CAPITAL LETTER S
0054; C; 0074; # LATIN CAPITAL LETTER T
0055; C; 0075; # LATIN CAPITAL LETTER U
0056; C; 0076; # LATIN CAPITAL LETTER V
0057; C; 0077; # LATIN CAPITAL LETTER W
0058; C; 0078; # LATIN CAPITAL LETTER X
0059; C; 0079; # LATIN CAPITAL LETTER Y
005A; C; 007A; # LATIN CAPITAL LETTER Z
00B5; C; 03BC; # MICRO SIGN
00C0; C; 00E0; # LATIN CAPITAL LETTER A WITH GRAVE
00C1; C; 00E1; # LATIN CAPITAL LETTER A WITH ACUTE
00C2; C; 00E2; # LATIN CAPITAL LETTER A WITH CIRCUMFLEX
00C3; C; 00E3; # LATIN CAPITAL LETTER A WITH TILDE
00C4; C; 00E4; # LATIN CAPITAL LETTER A WITH DIAERESIS
00C5; C; 00E5; # LATIN CAPITAL LETTER A WITH RING ABOVE
00C6; C; 00E6; # LATIN CAPITAL LETTER AE
00C7; C; 00E7; # LATIN CAPITAL LETTER C WITH CEDILLA
00C8; C; 00E8; # LATIN CAPITAL LETTER E WITH GRAVE
00C9; C; 00E9; # LATIN CAPITAL LETTER E WITH ACUTE
00CA; C; 00EA; # LATIN CAPITAL LETTER E WITH CIRCUMFLEX
00CB; C; 00EB; # LATIN CAPITAL LETTER E WITH DIAERESIS
00CC; C; 00EC; # LATIN CAPITAL LETTER I WITH GRAVE
00CD; C; 00ED; # LATIN CAPITAL LETTER I WITH ACUTE
00CE; C; 00EE; # LATIN CAPITAL LETTER I WITH CIRCUMFLEX
00CF; C; 00EF; # LATIN CAPITAL LETTER I WITH DIAERESIS
00D0; C; 00F0; # LATIN CAPITAL LETTER ETH
00D1; C; 00F1; # LATIN CAPITAL LETTER N WITH TILDE
00D2; C; 00F2; # LATIN CAPITAL LETTER O WITH GRAVE
00D3; C; 00F3; # LATIN CAPITAL LETTER O WITH ACUTE
00D4; C; 00F4; # LATIN CAPITAL LETTER O WITH CIRCUMFLEX
00D5; C; 00F5; # LATIN CAPITAL LETTER O WITH TILDE
00D6; C; 00F6; # LATIN CAPITAL LETTER O WITH DIAERESIS
00D8; C; 00F8; # LATIN CAPITAL LETTER O WITH STROKE
00D9; C; 00F9; # LATIN CAPITAL LETTER U WITH GRAVE
00DA; C; 00FA; # LATIN CAPITAL LETTER U WITH ACUTE
00DB; C; 00FB; # LATIN CAPITAL LETTER U WITH CIRCUMFLEX
00DC; C; 00FC; # LATIN CAPITAL LETTER U WITH DIAERESIS
00DD; C; 00FD; # LATIN CAPITAL LETTER Y WITH ACUTE
00DE; C; 00FE; # LATIN CAPITAL LETTER THORN
00DF; F; 0073 0073; # LATIN SMALL LETTER SHARP S
0100; C; 0101; # LATIN CAPITAL LETTER A WITH MACRON
0102; C; 0103; # LATIN CAPITAL LETTER A WITH BREVE
0104; C; 0105; # LATIN CAPITAL LETTER A WITH OGONEK
0106; C; 0107; # LATIN CAPITAL LETTER C WITH ACUTE
0108; C; 0109; # LATIN CAPITAL LETTER C WITH CIRCUMFLEX
010A; C; 010B; # LATIN CAPITAL LETTER C WITH DOT ABOVE
010C; C; 010D; # LATIN CAPITAL LETTER C WITH CARON
010E; C; 010F; # LATIN CAPITAL LETTER D WITH CARON
0110; C; 0111; # LATIN CAPITAL LETTER D WITH STROKE
0112; C; 0113; # LATIN CAPITAL LETTER E WITH MACRON
0114; C; 0115; # LATIN CAPITAL LETTER E WITH BREVE
0116; C; 0117; # LATIN CAPITAL LETTER E WITH DOT ABOVE
0118; C; 0119; # LATIN CAPITAL LETTER E WITH OGONEK
011A; C; 011B; # LATIN CAPITAL LETTER E WITH CARON
011C; C; 011D; # LATIN CAPITAL LETTER G WITH CIRCUMFLEX
011E; C; 011F; # LATIN CAPITAL LETTER G WITH BREVE
0120; C; 0121; # LATIN CAPITAL LETTER G WITH DOT ABOVE
0122; C; 0123; # LATIN CAPITAL LETTER G WITH CEDILLA
0124; C; 0125; # LATIN CAPITAL LETTER H WITH CIRCUMFLEX
0126; C; 0127; # LATIN CAPITAL LETTER H WITH STROKE
0128; C; 0129; # LATIN CAPITAL LETTER I WITH TILDE
012A; C; 012B; # LATIN CAPITAL LETTER I WITH MACRON
012C; C; 012D; # LATIN CAPITAL LETTER I WITH BREVE
012E; C; 012F; # LATIN CAPITAL LETTER I WITH OGONEK
0130; F; 0069 0307; # LATIN CAPITAL LETTER I WITH DOT ABOVE
0130; T; 0069; # LATIN CAPITAL LETTER I WITH DOT ABOVE
0132; C; 0133; # LATIN CAPITAL LIGATURE IJ
0134; C; 0135; # LATIN CAPITAL LETTER J WITH CIRCUMFLEX
0136; C; 0137; # LATIN CAPITAL LETTER K WITH CEDILLA
0139; C; 013A; # LATIN CAPITAL LETTER L WITH ACUTE
013B; C; 013C; # LATIN CAPITAL LETTER L WITH CEDILLA
013D; C; 013E; # LATIN CAPITAL LETTER L WITH CARON
013F; C; 0140; # LATIN CAPITAL LETTER L WITH MIDDLE DOT
0141; C; 0142; # LATIN CAPITAL LETTER L WITH STROKE
0143; C; 0144; # LATIN CAPITAL LETTER N WITH ACUTE
0145; C; 0146; # LATIN CAPITAL LETTER N WITH CEDILLA
0147; C; 0148; # LATIN CAPITAL LETTER N WITH CARON
0149; F; 02BC 006E; # LATIN SMALL LETTER N PRECEDED BY APOSTROPHE
014A; C; 014B; # LATIN CAPITAL LETTER ENG
014C; C; 014D; # LATIN CAPITAL LETTER O WITH MACRON
014E; C; 014F; # LATIN CAPITAL LETTER O WITH BREVE
0150; C; 0151; # LATIN CAPITAL LETTER O WITH DOUBLE ACUTE
0152; C; 0153; # LATIN CAPITAL LIGATURE OE
0154; C; 0155; # LATIN CAPITAL LETTER R WITH ACUTE
0156; C; 0157; # LATIN CAPITAL LETTER R WITH CEDILLA
0158; C; 0159; # LATIN CAPITAL LETTER R WITH CARON
015A; C; 015B; # LATIN CAPITAL LETTER S WITH ACUTE
015C; C; 015D; # LATIN CAPITAL LETTER S WITH CIRCUMFLEX
015E; C; 015F; # LATIN CAPITAL LETTER S WITH CEDILLA
0160; C; 0161; # LATIN CAPITAL LETTER S WITH CARON
0162; C; 0163; # LATIN CAPITAL LETTER T WITH CEDILLA
0164; C; 0165; # LATIN CAPITAL LETTER T WITH CARON
0166; C; 0167; # LATIN CAPITAL LETTER T WITH STROKE
0168; C; 0169; # LATIN CAPITAL LETTER U WITH TILDE
016A; C; 016B; # LATIN CAPITAL LETTER U WITH MACRON
016C; C; 016D; # LATIN CAPITAL LETTER U WITH BREVE
016E; C; 016F; # LATIN CAPITAL LETTER U WITH RING ABOVE
0170; C; 0171; # LATIN CAPITAL LETTER U WITH DOUBLE ACUTE
0172; C; 0173; # LATIN CAPITAL LETTER U WITH OGONEK
0174; C; 0175; # LATIN CAPITAL LETTER W WITH CIRCUMFLEX
0176; C; 0177; # LATIN CAPITAL LETTER Y WITH CIRCUMFLEX
0178; C; 00FF; # LATIN CAPITAL LETTER Y WITH DIAERESIS
0179; C; 017A; # LATIN CAPITAL LETTER Z WITH ACUTE
017B; C; 017C; # LATIN CAPITAL LETTER Z WITH DOT ABOVE
017D; C; 017E; # LATIN CAPITAL LETTER Z WITH CARON
017F; C; 0073; # LATIN SMALL LETTER LONG S
0181; C; 0253; # LATIN CAPITAL LETTER B WITH HOOK
0182; C; 0183; # LATIN CAPITAL LETTER B WITH TOPBAR
0184; C; 0185; # LATIN CAPITAL LETTER TONE SIX
0186; C; 0254; # LATIN CAPITAL LETTER OPEN O
0187; C; 0188; # LATIN CAPITAL LETTER C WITH HOOK
0189; C; 0256; # LATIN CAPITAL LETTER AFRICAN D
018A; C; 0257; # LATIN CAPITAL LETTER D WITH HOOK
018B; C; 018C; # LATIN CAPITAL LETTER D WITH TOPBAR
018E; C; 01DD; # LATIN CAPITAL LETTER REVERSED E
018F; C; 0259; # LATIN CAPITAL LETTER SCHWA
0190; C; 025B; # LATIN CAPITAL LETTER OPEN E
0191; C; 0192; # LATIN CAPITAL LETTER F WITH HOOK
0193; C; 0260; # LATIN CAPITAL LETTER G WITH HOOK
0194; C; 0263; # LATIN CAPITAL LETTER GAMMA
0196; C; 0269; # LATIN CAPITAL LETTER IOTA
0197; C; 0268; # LATIN CAPITAL LETTER I WITH STROKE
0198; C; 0199; # LATIN CAPITAL LETTER K WITH HOOK
019C; C; 026F; # LATIN CAPITAL LETTER TURNED M
019D; C; 0272; # LATIN CAPITAL LETTER N WITH LEFT HOOK
019F; C; 0275; # LATIN CAPITAL LETTER O WITH MIDDLE TILDE
01A0; C; 01A1; # LATIN CAPITAL LETTER O WITH HORN
01A2; C; 01A3; # LATIN CAPITAL LETTER OI
01A4; C; 01A5; # LATIN CAPITAL LETTER P WITH HOOK
01A6; C; 0280; # LATIN LETTER YR
01A7; C; 01A8; # LATIN CAPITAL LETTER TONE TWO
01A9; C; 0283; # LATIN CAPITAL LETTER ESH
01AC; C; 01AD; # LATIN CAPITAL LETTER T WITH HOOK
01AE; C; 0288; # LATIN CAPITAL LETTER T WITH RETROFLEX HOOK
01AF; C; 01B0; # LATIN CAPITAL LETTER U WITH HORN
01B1; C; 028A; # LATIN CAPITAL LETTER UPSILON
01B2; C; 028B; # LATIN CAPITAL LETTER V WITH HOOK
01B3; C; 01B4; # LATIN CAPITAL LETTER Y WITH HOOK
01B5; C; 01B6; # LATIN CAPITAL LETTER Z WITH STROKE
01B7; C; 0292; # LATIN CAPITAL LETTER EZH
01B8; C; 01B9; # LATIN CAPITAL LETTER EZH REVERSED
01BC; C; 01BD; # LATIN CAPITAL LETTER TONE FIVE
01C4; C; 01C6; # LATIN CAPITAL LETTER DZ WITH CARON
01C5; C; 01C6; # LATIN CAPITAL LETTER D WITH SMALL LETTER Z WITH CARON
01C7; C; 01C9; # LATIN CAPITAL LETTER LJ
01C8; C; 01C9; # LATIN CAPITAL LETTER L WITH SMALL LETTER J
01CA; C; 01CC; # LATIN CAPITAL LETTER NJ
01CB; C; 01CC; # LATIN CAPITAL LETTER N WITH SMALL LETTER J
01CD; C; 01CE; # LATIN CAPITAL LETTER A WITH CARON
01CF; C; 01D0; # LATIN CAPITAL LETTER I WITH CARON
01D1; C; 01D2; # LATIN CAPITAL LETTER O WITH CARON
01D3; C; 01D4; # LATIN CAPITAL LETTER U WITH CARON
01D5; C; 01D6; # LATIN CAPITAL LETTER U WITH DIAERESIS AND MACRON
01D7; C; 01D8; # LATIN CAPITAL LETTER U WITH DIAERESIS AND ACUTE
01D9; C; 01DA; # LATIN CAPITAL LETTER U WITH DIAERESIS AND CARON
01DB; C; 01DC; # LATIN CAPITAL LETTER U WITH DIAERESIS AND GRAVE
01DE; C; 01DF; # LATIN CAPITAL LETTER A WITH DIAERESIS AND MACRON
01E0; C; 01E1; # LATIN CAPITAL LETTER A WITH DOT ABOVE AND MACRON
01E2; C; 01E3; # LATIN CAPITAL LETTER AE WITH MACRON
01E4; C; 01E5; # LATIN CAPITAL LETTER G WITH STROKE
01E6; C; 01E7; # LATIN CAPITAL LETTER G WITH CARON
01E8; C; 01E9; # LATIN CAPITAL LETTER K WITH CARON
01EA; C; 01EB; # LATIN CAPITAL LETTER O WITH OGONEK
01EC; C; 01ED; # LATIN CAPITAL LETTER O WITH OGONEK AND MACRON
01EE; C; 01EF; # LATIN CAPITAL LETTER EZH WITH CARON
01F0; F; 006A 030C; # LATIN SMALL LETTER J WITH CARON
01F1; C; 01F3; # LATIN CAPITAL LETTER DZ
01F2; C; 01F3; # LATIN CAPITAL LETTER D WITH SMALL LETTER Z
01F4; C; 01F5; # LATIN CAPITAL LETTER G WITH ACUTE
01F6; C; 0195; # LATIN CAPITAL LETTER HWAIR
01F7; C; 01BF; # LATIN CAPITAL LETTER WYNN
01F8; C; 01F9; # LATIN CAPITAL LETTER N WITH GRAVE
01FA; C; 01FB; # LATIN CAPITAL LETTER A WITH RING ABOVE AND ACUTE
01FC; C; 01FD; # LATIN CAPITAL LETTER AE WITH ACUTE
01FE; C; 01FF; # LATIN CAPITAL LETTER O WITH STROKE AND ACUTE
0200; C; 0201; # LATIN CAPITAL LETTER A WITH DOUBLE GRAVE
0202; C; 0203; # LATIN CAPITAL LETTER A WITH INVERTED BREVE
0204; C; 0205; # LATIN CAPITAL LETTER E WITH DOUBLE GRAVE
0206; C; 0207; # LATIN CAPITAL LETTER E WITH INVERTED BREVE
0208; C; 0209; # LATIN CAPITAL LETTER I WITH DOUBLE GRAVE
020A; C; 020B; # LATIN CAPITAL LETTER I WITH INVERTED BREVE
020C; C; 020D; # LATIN CAPITAL LETTER O WITH DOUBLE GRAVE
020E; C; 020F; # LATIN CAPITAL LETTER O WITH INVERTED BREVE
0210; C; 0211; # LATIN CAPITAL LETTER R WITH DOUBLE GRAVE
0212; C; 0213; # LATIN CAPITAL LETTER R WITH INVERTED BREVE
0214; C; 0215; # LATIN CAPITAL LETTER U WITH DOUBLE GRAVE
0216; C; 0217; # LATIN CAPITAL LETTER U WITH INVERTED BREVE
0218; C; 0219; # LATIN CAPITAL LETTER S WITH COMMA BELOW
021A; C; 021B; # LATIN CAPITAL LETTER T WITH COMMA BELOW
021C; C; 021D; # LATIN CAPITAL LETTER YOGH
021E; C; 021F; # LATIN CAPITAL LETTER H WITH CARON
0220; C; 019E; # LATIN CAPITAL LETTER N WITH LONG RIGHT LEG
0222; C; 0223; # LATIN CAPITAL LETTER OU
0224; C; 0225; # LATIN CAPITAL LETTER Z WITH HOOK
0226; C; 0227; # LATIN CAPITAL LETTER A WITH DOT ABOVE
0228; C; 0229; # LATIN CAPITAL LETTER E WITH CEDILLA
022A; C; 022B; # LATIN CAPITAL LETTER O WITH DIAERESIS AND MACRON
022C; C; 022D; # LATIN CAPITAL LETTER O WITH TILDE AND MACRON
022E; C; 022F; # LATIN CAPITAL LETTER O WITH DOT ABOVE
0230; C; 0231; # LATIN CAPITAL LETTER O WITH DOT ABOVE AND MACRON
0232; C; 0233; # LATIN CAPITAL LETTER Y WITH MACRON
023A; C; 2C65; # LATIN CAPITAL LETTER A WITH STROKE
023B; C; 023C; # LATIN CAPITAL LETTER C WITH STROKE
023D; C; 019A; # LATIN CAPITAL LETTER L WITH BAR
023E; C; 2C66; # LATIN CAPITAL LETTER T WITH DIAGONAL STROKE
0241; C; 0242; # LATIN CAPITAL LETTER GLOTTAL STOP
0243; C; 0180; # LATIN CAPITAL LETTER B WITH STROKE
0244; C; 0289; # LATIN CAPITAL LETTER U BAR
0245; C; 028C; # LATIN CAPITAL LETTER TURNED V
0246; C; 0247; # LATIN CAPITAL LETTER E WITH STROKE
0248; C; 0249; # LATIN CAPITAL LETTER J WITH STROKE
024A; C; 024B; # LATIN CAPITAL LETTER SMALL Q WITH HOOK TAIL
024C; C; 024D; # LATIN CAPITAL LETTER R WITH STROKE
024E; C; 024F; # LATIN CAPITAL LETTER Y WITH STROKE
0345; C; 03B9; # COMBINING GREEK YPOGEGRAMMENI
0370; C; 0371; # GREEK CAPITAL LETTER HETA
0372; C; 0373; # GREEK CAPITAL LETTER ARCHAIC SAMPI
0376; C; 0377; # GREEK CAPITAL LETTER PAMPHYLIAN DIGAMMA
037F; C; 03F3; # GREEK CAPITAL LETTER YOT
0386; C; 03AC; # GREEK CAPITAL LETTER ALPHA WITH TONOS
0388; C; 03AD; # GREEK CAPITAL LETTER EPSILON WITH TONOS
0389; C; 03AE; # GREEK CAPITAL LETTER ETA WITH TONOS
038A; C; 03AF; # GREEK CAPITAL LETTER IOTA WITH TONOS
038C; C; 03CC; # GREEK CAPITAL LETTER OMICRON WITH TONOS
038E; C; 03CD; # GREEK CAPITAL LETTER UPSILON WITH TONOS
038F; C; 03CE; # GREEK CAPITAL LETTER OMEGA WITH TONOS
0390; F; 03B9 0308 0301; # GREEK SMALL LETTER IOTA WITH DIALYTIKA AND TONOS
0391; C; 03B1; # GREEK CAPITAL LETTER ALPHA
0392; C; 03B2; # GREEK CAPITAL LETTER BETA
0393; C; 03B3; # GREEK CAPITAL LETTER GAMMA
0394; C; 03B4; # GREEK CAPITAL LETTER DELTA
0395; C; 03B5; # GREEK CAPITAL LETTER EPSILON
0396; C; 03B6; # GREEK CAPITAL LETTER ZETA
0397; C; 03B7; # GREEK CAPITAL LETTER ETA
0398; C; 03B8; # GREEK CAPITAL LETTER THETA
0399; C; 03B9; # GREEK CAPITAL LETTER IOTA
039A; C; 03BA; # GREEK CAPITAL LETTER KAPPA
039B; C; 03BB; # GREEK CAPITAL LETTER LAMDA
039C; C; 03BC; # GREEK CAPITAL LETTER MU
039D; C; 03BD; # GREEK CAPITAL LETTER NU
039E; C; 03BE; # GREEK CAPITAL LETTER XI
039F; C; 03BF; # GREEK CAPITAL LETTER OMICRON
03A0; C; 03C0; # GREEK CAPITAL LETTER PI
03A1; C; 03C1; # GREEK CAPITAL LETTER RHO
03A3; C; 03C3; # GREEK CAPITAL LETTER SIGMA
03A4; C; 03C4; # GREEK CAPITAL LETTER TAU
03A5; C; 03C5; # GREEK CAPITAL LETTER UPSILON
03A6; C; 03C6; # GREEK CAPITAL LETTER PHI
03A7; C; 03C7; # GREEK CAPITAL LETTER CHI
03A8; C; 03C8; # GREEK CAPITAL LETTER PSI
03A9; C; 03C9; # GREEK CAPITAL LETTER OMEGA
03AA; C; 03CA; # GREEK CAPITAL LETTER IOTA WITH DIALYTIKA
03AB; C; 03CB; # GREEK CAPITAL LETTER UPSILON WITH DIALYTIKA
03B0; F; 03C5 0308 0301; # GREEK SMALL LETTER UPSILON WITH DIALYTIKA AND TONOS
03C2; C; 03C3; # GREEK SMALL LETTER FINAL SIGMA
03CF; C; 03D7; # GREEK CAPITAL KAI SYMBOL
03D0; C; 03B2; # GREEK BETA SYMBOL
03D1; C; 03B8; # GREEK THETA SYMBOL
03D5; C; 03C6; # GREEK PHI SYMBOL
03D6; C; 03C0; # GREEK PI SYMBOL
03D8; C; 03D9; # GREEK LETTER ARCHAIC KOPPA
03DA; C; 03DB; # GREEK LETTER STIGMA
03DC; C; 03DD; # GREEK LETTER DIGAMMA
03DE; C; 03DF; # GREEK LETTER KOPPA
03E0; C; 03E1; # GREEK LETTER SAMPI
03E2; C; 03E3; # COPTIC CAPITAL LETTER SHEI
03E4; C; 03E5; # COPTIC CAPITAL LETTER FEI
03E6; C; 03E7; # COPTIC CAPITAL LETTER KHEI
03E8; C; 03E9; # COPTIC CAPITAL LETTER HORI
03EA; C; 03EB; # COPTIC CAPITAL LETTER GANGIA
03EC; C; 03ED; # COPTIC CAPITAL LETTER SHIMA
03EE; C; 03EF; # COPTIC CAPITAL LETTER DEI
03F0; C; 03BA; # GREEK KAPPA SYMBOL
03F1; C; 03C1; # GREEK RHO SYMBOL
03F4; C; 03B8; # GREEK CAPITAL THETA SYMBOL
03F5; C; 03B5; # GREEK LUNATE EPSILON SYMBOL
03F7; C; 03F8; # GREEK CAPITAL LETTER SHO
03F9; C; 03F2; # GREEK CAPITAL LUNATE SIGMA SYMBOL
03FA; C; 03FB; # GREEK CAPITAL LETTER SAN
03FD; C; 037B; # GREEK CAPITAL REVERSED LUNATE SIGMA SYMBOL
03FE; C; 037C; # GREEK CAPITAL DOTTED LUNATE SIGMA SYMBOL
03FF; C; 037D; # GREEK CAPITAL REVERSED DOTTED LUNATE SIGMA SYMBOL
0400; C; 0450; # CYRILLIC CAPITAL LETTER IE WITH GRAVE
0401; C; 0451; # CYRILLIC CAPITAL LETTER IO
0402; C; 0452; # CYRILLIC CAPITAL LETTER DJE
0403; C; 0453; # CYRILLIC CAPITAL LETTER GJE
0404; C; 0454; # CYRILLIC CAPITAL LETTER UKRAINIAN IE
0405; C; 0455; # CYRILLIC CAPITAL LETTER DZE
0406; C; 0456; # CYRILLIC CAPITAL LETTER BYELORUSSIAN-UKRAINIAN I
0407; C; 0457; # CYRILLIC CAPITAL LETTER YI
0408; C; 0458; # CYRILLIC CAPITAL LETTER JE
0409; C; 0459; # CYRILLIC CAPITAL LETTER LJE
040A; C; 045A; # CYRILLIC CAPITAL LETTER NJE
040B; C; 045B; # CYRILLIC CAPITAL LETTER TSHE
040C; C; 045C; # CYRILLIC CAPITAL LETTER KJE
040D; C; 045D; # CYRILLIC CAPITAL LETTER I WITH GRAVE
040E; C; 045E; # CYRILLIC CAPITAL LETTER SHORT U
040F; C; 045F; # CYRILLIC CAPITAL LETTER DZHE
0410; C; 0430; # CYRILLIC CAPITAL LETTER A
0411; C; 0431; # CYRILLIC CAPITAL LETTER BE
0412; C; 0432; # CYRILLIC CAPITAL LETTER VE
0413; C; 0433; # CYRILLIC CAPITAL LETTER GHE
0414; C; 0434; # CYRILLIC CAPITAL LETTER DE
0415; C; 0435; # CYRILLIC CAPITAL LETTER IE
0416; C; 0436; # CYRILLIC CAPITAL LETTER ZHE
0417; C; 0437; # CYRILLIC CAPITAL LETTER ZE
0418; C; 0438; # CYRILLIC CAPITAL LETTER I
0419; C; 0439; # CYRILLIC CAPITAL LETTER SHORT I
041A; C; 043A; # CYRILLIC CAPITAL LETTER KA
041B; C; 043B; # CYRILLIC CAPITAL LETTER EL
041C; C; 043C; # CYRILLIC CAPITAL LETTER EM
041D; C; 043D; # CYRILLIC CAPITAL LETTER EN
041E; C; 043E; # CYRILLIC CAPITAL LETTER O
041F; C; 043F; # CYRILLIC CAPITAL LETTER PE
0420; C; 0440; # CYRILLIC CAPITAL LETTER ER
0421; C; 0441; # CYRILLIC CAPITAL LETTER ES
0422; C; 0442; # CYRILLIC CAPITAL LETTER TE
0423; C; 0443; # CYRILLIC CAPITAL LETTER U
0424; C; 0444; # CYRILLIC CAPITAL LETTER EF
0425; C; 0445; # CYRILLIC CAPITAL LETTER HA
0426; C; 0446; # CYRILLIC CAPITAL LETTER TSE
0427; C; 0447; # CYRILLIC CAPITAL LETTER CHE
0428; C; 0448; # CYRILLIC CAPITAL LETTER SHA
0429; C; 0449; # CYRILLIC CAPITAL LETTER SHCHA
042A; C; 044A; # CYRILLIC CAPITAL LETTER HARD SIGN
042B; C; 044B; # CYRILLIC CAPITAL LETTER YERU
042C; C; 044C; # CYRILLIC CAPITAL LETTER SOFT SIGN
042D; C; 044D; # CYRILLIC CAPITAL LETTER E
042E; C; 044E; # CYRILLIC CAPITAL LETTER YU
042F; C; 044F; # CYRILLIC CAPITAL LETTER YA
0460; C; 0461; # CYRILLIC CAPITAL LETTER OMEGA
0462; C; 0463; # CYRILLIC CAPITAL LETTER YAT
0464; C; 0465; # CYRILLIC CAPITAL LETTER IOTIFIED E
0466; C; 0467; # CYRILLIC CAPITAL LETTER LITTLE YUS
0468; C; 0469; # CYRILLIC CAPITAL LETTER IOTIFIED LITTLE YUS
046A; C; 046B; # CYRILLIC CAPITAL LETTER BIG YUS
046C; C; 046D; # CYRILLIC CAPITAL LETTER IOTIFIED BIG YUS
046E; C; 046F; # CYRILLIC CAPITAL LETTER KSI
0470; C; 0471; # CYRILLIC CAPITAL LETTER PSI
0472; C; 0473; # CYRILLIC CAPITAL LETTER FITA
0474; C; 0475; # CYRILLIC CAPITAL LETTER IZHITSA
0476; C; 0477; # CYRILLIC CAPITAL LETTER IZHITSA WITH DOUBLE GRAVE ACCENT
0478; C; 0479; # CYRILLIC CAPITAL LETTER UK
047A; C; 047B; # CYRILLIC CAPITAL LETTER ROUND OMEGA
047C; C; 047D; # CYRILLIC CAPITAL LETTER OMEGA WITH TITLO
047E; C; 047F; # CYRILLIC CAPITAL LETTER OT
0480; C; 0481; # CYRILLIC CAPITAL LETTER KOPPA
048A; C; 048B; # CYRILLIC CAPITAL LETTER SHORT I WITH TAIL
048C; C; 048D; # CYRILLIC CAPITAL LETTER SEMISOFT SIGN
048E; C; 048F; # CYRILLIC CAPITAL LETTER ER WITH TICK
0490; C; 0491; # CYRILLIC CAPITAL LETTER GHE WITH UPTURN
0492; C; 0493; # CYRILLIC CAPITAL LETTER GHE WITH STROKE
0494; C; 0495; # CYRILLIC CAPITAL LETTER GHE WITH MIDDLE HOOK
0496; C; 0497; # CYRILLIC CAPITAL LETTER ZHE WITH DESCENDER
0498; C; 0499; # CYRILLIC CAPITAL LETTER ZE WITH DESCENDER
049A; C; 049B; # CYRILLIC CAPITAL LETTER KA WITH DESCENDER
049C; C; 049D; # CYRILLIC CAPITAL LETTER KA WITH VERTICAL STROKE
049E; C; 049F; # CYRILLIC CAPITAL LETTER KA WITH STROKE
04A0; C; 04A1; # CYRILLIC CAPITAL LETTER BASHKIR KA
04A2; C; 04A3; # CYRILLIC CAPITAL LETTER EN WITH DESCENDER
04A4; C; 04A5; # CYRILLIC CAPITAL LIGATURE EN GHE
04A6; C; 04A7; # CYRILLIC CAPITAL LETTER PE WITH MIDDLE HOOK
04A8; C; 04A9; # CYRILLIC CAPITAL LETTER ABKHASIAN HA
04AA; C; 04AB; # CYRILLIC CAPITAL LETTER ES WITH DESCENDER
04AC; C; 04AD; # CYRILLIC CAPITAL LETTER TE WITH DESCENDER
04AE; C; 04AF; # CYRILLIC CAPITAL LETTER STRAIGHT U
04B0; C; 04B1; # CYRILLIC CAPITAL LETTER STRAIGHT U WITH STROKE
04B2; C; 04B3; # CYRILLIC CAPITAL LETTER HA WITH DESCENDER
04B4; C; 04B5; # CYRILLIC CAPITAL LIGATURE TE TSE
04B6; C; 04B7; # CYRILLIC CAPITAL LETTER CHE WITH DESCENDER
04B8; C; 04B9; # CYRILLIC CAPITAL LETTER CHE WITH VERTICAL STROKE
04BA; C; 04BB; # CYRILLIC CAPITAL LETTER SHHA
04BC; C; 04BD; # CYRILLIC CAPITAL LETTER ABKHASIAN CHE
04BE; C; 04BF; # CYRILLIC CAPITAL LETTER ABKHASIAN CHE WITH DESCENDER
04C0; C; 04CF; # CYRILLIC LETTER PALOCHKA
04C1; C; 04C2; # CYRILLIC CAPITAL LETTER ZHE WITH BREVE
04C3; C; 04C4; # CYRILLIC CAPITAL LETTER KA WITH HOOK
04C5; C; 04C6; # CYRILLIC CAPITAL LETTER EL WITH TAIL
04C7; C; 04C8; # CYRILLIC CAPITAL LETTER EN WITH HOOK
04C9; C; 04CA; # CYRILLIC CAPITAL LETTER EN WITH TAIL
04CB; C; 04CC; # CYRILLIC CAPITAL LETTER KHAKASSIAN CHE
04CD; C; 04CE; # CYRILLIC CAPITAL LETTER EM WITH TAIL
04D0; C; 04D1; # CYRILLIC CAPITAL LETTER A WITH BREVE
04D2; C; 04D3; # CYRILLIC CAPITAL LETTER A WITH DIAERESIS
04D4; C; 04D5; # CYRILLIC CAPITAL LIGATURE A IE
04D6; C; 04D7; # CYRILLIC CAPITAL LETTER IE WITH BREVE
04D8; C; 04D9; # CYRILLIC CAPITAL LETTER SCHWA
04DA; C; 04DB; # CYRILLIC CAPITAL LETTER SCHWA WITH DIAERESIS
04DC; C; 04DD; # CYRILLIC CAPITAL LETTER ZHE WITH DIAERESIS
04DE; C; 04DF; # CYRILLIC CAPITAL LETTER ZE WITH DIAERESIS
04E0; C; 04E1; # CYRILLIC CAPITAL LETTER ABKHASIAN DZE
04E2; C; 04E3; # CYRILLIC CAPITAL LETTER I WITH MACRON
04E4; C; 04E5; # CYRILLIC CAPITAL LETTER I WITH DIAERESIS
04E6; C; 04E7; # CYRILLIC CAPITAL LETTER O WITH DIAERESIS
04E8; C; 04E9; # CYRILLIC CAPITAL LETTER BARRED O
04EA; C; 04EB; # CYRILLIC CAPITAL LETTER BARRED O WITH DIAERESIS
04EC; C; 04ED; # CYRILLIC CAPITAL LETTER E WITH DIAERESIS
04EE; C; 04EF; # CYRILLIC CAPITAL LETTER U WITH MACRON
04F0; C; 04F1; # CYRILLIC CAPITAL LETTER U WITH DIAERESIS
04F2; C; 04F3; # CYRILLIC CAPITAL LETTER U WITH DOUBLE ACUTE
04F4; C; 04F5; # CYRILLIC CAPITAL LETTER CHE WITH DIAERESIS
04F6; C; 04F7; # CYRILLIC CAPITAL LETTER GHE WITH DESCENDER
04F8; C; 04F9; # CYRILLIC CAPITAL LETTER YERU WITH DIAERESIS
04FA; C; 04FB; # CYRILLIC CAPITAL LETTER GHE WITH STROKE AND HOOK
04FC; C; 04FD; # CYRILLIC CAPITAL LETTER HA WITH HOOK
04FE; C; 04FF; # CYRILLIC CAPITAL LETTER HA WITH STROKE
0500; C; 0501; # CYRILLIC CAPITAL LETTER KOMI DE
0502; C; 0503; # CYRILLIC CAPITAL LETTER KOMI DJE
0504; C; 0505; # CYRILLIC CAPITAL LETTER KOMI ZJE
0506; C; 0507; # CYRILLIC CAPITAL LETTER KOMI DZJE
0508; C; 0509; # CYRILLIC CAPITAL LETTER KOMI LJE
050A; C; 050B; # CYRILLIC CAPITAL LETTER KOMI NJE
050C; C; 050D; # CYRILLIC CAPITAL LETTER KOMI SJE
050E; C; 050F; # CYRILLIC CAPITAL LETTER KOMI TJE
0510; C; 0511; # CYRILLIC CAPITAL LETTER REVERSED ZE
0512; C; 0513; # CYRILLIC CAPITAL LETTER EL WITH HOOK
0514; C; 0515; # CYRILLIC CAPITAL LETTER LHA
0516; C; 0517; # CYRILLIC CAPITAL LETTER RHA
0518; C; 0519; # CYRILLIC CAPITAL LETTER YAE
051A; C; 051B; # CYRILLIC CAPITAL LETTER QA
051C; C; 051D; # CYRILLIC CAPITAL LETTER WE
051E; C; 051F; # CYRILLIC CAPITAL LETTER ALEUT KA
0520; C; 0521; # CYRILLIC CAPITAL LETTER EL WITH MIDDLE HOOK
0522; C; 0523; # CYRILLIC CAPITAL LETTER EN WITH MIDDLE HOOK
0524; C; 0525; # CYRILLIC CAPITAL LETTER PE WITH DESCENDER
0526; C; 0527; # CYRILLIC CAPITAL LETTER SHHA WITH DESCENDER
0528; C; 0529; # CYRILLIC CAPITAL LETTER EN WITH LEFT HOOK
052A; C; 052B; # CYRILLIC CAPITAL LETTER DZZHE
052C; C; 052D; # CYRILLIC CAPITAL LETTER DCHE
052E; C; 052F; # CYRILLIC CAPITAL LETTER EL WITH DESCENDER
0531; C; 0561; # ARMENIAN CAPITAL LETTER AYB
0532; C; 0562; # ARMENIAN CAPITAL LETTER BEN
0533; C; 0563; # ARMENIAN CAPITAL LETTER GIM
0534; C; 0564; # ARMENIAN CAPITAL LETTER DA
0535; C; 0565; # ARMENIAN CAPITAL LETTER ECH
0536; C; 0566; # ARMENIAN CAPITAL LETTER ZA
0537; C; 0567; # ARMENIAN CAPITAL LETTER EH
0538; C; 0568; # ARMENIAN CAPITAL LETTER ET
0539; C; 0569; # ARMENIAN CAPITAL LETTER TO
053A; C; 056A; # ARMENIAN CAPITAL LETTER ZHE
053B; C; 056B; # ARMENIAN CAPITAL LETTER INI
053C; C; 056C; # ARMENIAN CAPITAL LETTER LIWN
053D; C; 056D; # ARMENIAN CAPITAL LETTER XEH
053E; C; 056E; # ARMENIAN CAPITAL LETTER CA
053F; C; 056F; # ARMENIAN CAPITAL LETTER KEN
0540; C; 0570; # ARMENIAN CAPITAL LETTER HO
0541; C; 0571; # ARMENIAN CAPITAL LETTER JA
0542; C; 0572; # ARMENIAN CAPITAL LETTER GHAD
0543; C; 0573; # ARMENIAN CAPITAL LETTER CHEH
0544; C; 0574; # ARMENIAN CAPITAL LETTER MEN
0545; C; 0575; # ARMENIAN CAPITAL LETTER YI
0546; C; 0576; # ARMENIAN CAPITAL LETTER NOW
0547; C; 0577; # ARMENIAN CAPITAL LETTER SHA
0548; C; 0578; # ARMENIAN CAPITAL LETTER VO
0549; C; 0579; # ARMENIAN CAPITAL LETTER CHA
054A; C; 057A; # ARMENIAN CAPITAL LETTER PEH
054B; C; 057B; # ARMENIAN CAPITAL LETTER JHEH
054C; C; 057C; # ARMENIAN CAPITAL LETTER RA
054D; C; 057D; # ARMENIAN CAPITAL LETTER SEH
054E; C; 057E; # ARMENIAN CAPITAL LETTER VEW
054F; C; 057F; # ARMENIAN CAPITAL LETTER TIWN
0550; C; 0580; # ARMENIAN CAPITAL LETTER REH
0551; C; 0581; # ARMENIAN CAPITAL LETTER CO
0552; C; 0582; # ARMENIAN CAPITAL LETTER YIWN
0553; C; 0583; # ARMENIAN CAPITAL LETTER PIWR
0554; C; 0584; # ARMENIAN CAPITAL LETTER KEH
0555; C; 0585; # ARMENIAN CAPITAL LETTER OH
0556; C; 0586; # ARMENIAN CAPITAL LETTER FEH
0587; F; 0565 0582; # ARMENIAN SMALL LIGATURE ECH YIWN
10A0; C; 2D00; # GEORGIAN CAPITAL LETTER AN
10A1; C; 2D01; # GEORGIAN CAPITAL LETTER BAN
10A2; C; 2D02; # GEORGIAN CAPITAL LETTER GAN
10A3; C; 2D03; # GEORGIAN CAPITAL LETTER DON
10A4; C; 2D04; # GEORGIAN CAPITAL LETTER EN
10A5; C; 2D05; # GEORGIAN CAPITAL LETTER VIN
10A6; C; 2D06; # GEORGIAN CAPITAL LETTER ZEN
10A7; C; 2D07; # GEORGIAN CAPITAL LETTER TAN
10A8; C; 2D08; # GEORGIAN CAPITAL LETTER IN
10A9; C; 2D09; # GEORGIAN CAPITAL LETTER KAN
10AA; C; 2D0A; # GEORGIAN CAPITAL LETTER LAS
10AB; C; 2D0B; # GEORGIAN CAPITAL LETTER MAN
10AC; C; 2D0C; # GEORGIAN CAPITAL LETTER NAR
10AD; C; 2D0D; # GEORGIAN CAPITAL LETTER ON
10AE; C; 2D0E; # GEORGIAN CAPITAL LETTER PAR
10AF; C; 2D0F; # GEORGIAN CAPITAL LETTER ZHAR
10B0; C; 2D10; # GEORGIAN CAPITAL LETTER RAE
10B1; C; 2D11; # GEORGIAN CAPITAL LETTER SAN
10B2; C; 2D12; # GEORGIAN CAPITAL LETTER TAR
10B3; C; 2D13; # GEORGIAN CAPITAL LETTER UN
10B4; C; 2D14; # GEORGIAN CAPITAL LETTER PHAR
10B5; C; 2D15; # GEORGIAN CAPITAL LETTER KHAR
10B6; C; 2D16; # GEORGIAN CAPITAL LETTER GHAN
10B7; C; 2D17; # GEORGIAN CAPITAL LETTER QAR
10B8; C; 2D18; # GEORGIAN CAPITAL LETTER SHIN
10B9; C; 2D19; # GEORGIAN CAPITAL LETTER CHIN
10BA; C; 2D1A; # GEORGIAN CAPITAL LETTER CAN
10BB; C; 2D1B; # GEORGIAN CAPITAL LETTER JIL
10BC; C; 2D1C; # GEORGIAN CAPITAL LETTER CIL
10BD; C; 2D1D; # GEORGIAN CAPITAL LETTER CHAR
10BE; C; 2D1E; # GEORGIAN CAPITAL LETTER XAN
10BF; C; 2D1F; # GEORGIAN CAPITAL LETTER JHAN
10C0; C; 2D20; # GEORGIAN CAPITAL LETTER HAE
10C1; C; 2D21; # GEORGIAN CAPITAL LETTER HE
10C2; C; 2D22; # GEORGIAN CAPITAL LETTER HIE
10C3; C; 2D23; # GEORGIAN CAPITAL LETTER WE
10C4; C; 2D24; # GEORGIAN CAPITAL LETTER HAR
10C5; C; 2D25; # GEORGIAN CAPITAL LETTER HOE
10C7; C; 2D27; # GEORGIAN CAPITAL LETTER YN
10CD; C; 2D2D; # GEORGIAN CAPITAL LETTER AEN
13F8; C; 13F0; # CHEROKEE SMALL LETTER YE
13F9; C; 13F1; # CHEROKEE SMALL LETTER YI
13FA; C; 13F2; # CHEROKEE SMALL LETTER YO
13FB; C; 13F3; # CHEROKEE SMALL LETTER YU
13FC; C; 13F4; # CHEROKEE SMALL LETTER YV
13FD; C; 13F5; # CHEROKEE SMALL LETTER MV
1C80; C; 0432; # CYRILLIC SMALL LETTER ROUNDED VE
1C81; C; 0434; # CYRILLIC SMALL LETTER LONG-LEGGED DE
1C82; C; 043E; # CYRILLIC SMALL LETTER NARROW O
1C83; C; 0441; # CYRILLIC SMALL LETTER WIDE ES
1C84; C; 0442; # CYRILLIC SMALL LETTER TALL TE
1C85; C; 0442; # CYRILLIC SMALL LETTER THREE-LEGGED TE
1C86; C; 044A; # CYRILLIC SMALL LETTER TALL HARD SIGN
1C87; C; 0463; # CYRILLIC SMALL LETTER TALL YAT
1C88; C; A64B; # CYRILLIC SMALL LETTER UNBLENDED UK
1C90; C; 10D0; # GEORGIAN MTAVRULI CAPITAL LETTER AN
1C91; C; 10D1; # GEORGIAN MTAVRULI CAPITAL LETTER BAN
1C92; C; 10D2; # GEORGIAN MTAVRULI CAPITAL LETTER GAN
1C93; C; 10D3; # GEORGIAN MTAVRULI CAPITAL LETTER DON
1C94; C; 10D4; # GEORGIAN MTAVRULI CAPITAL LETTER EN
1C95; C; 10D5; # GEORGIAN MTAVRULI CAPITAL LETTER VIN
1C96; C; 10D6; # GEORGIAN MTAVRULI CAPITAL LETTER ZEN
1C97; C; 10D7; # GEORGIAN MTAVRULI CAPITAL LETTER TAN
1C98; C; 10D8; # GEORGIAN MTAVRULI CAPITAL LETTER IN
1C99; C; 10D9; # GEORGIAN MTAVRULI CAPITAL LETTER KAN
1C9A; C; 10DA; # GEORGIAN MTAVRULI CAPITAL LETTER LAS
1C9B; C; 10DB; # GEORGIAN MTAVRULI CAPITAL LETTER MAN
1C9C; C; 10DC; # GEORGIAN MTAVRULI CAPITAL LETTER NAR
1C9D; C; 10DD; # GEORGIAN MTAVRULI CAPITAL LETTER ON
1C9E; C; 10DE; # GEORGIAN MTAVRULI CAPITAL LETTER PAR
1C9F; C; 10DF; # GEORGIAN MTAVRULI CAPITAL LETTER ZHAR
1CA0; C; 10E0; # GEORGIAN MTAVRULI CAPITAL LETTER RAE
1CA1; C; 10E1; # GEORGIAN MTAVRULI CAPITAL LETTER SAN
1CA2; C; 10E2; # GEORGIAN MTAVRULI CAPITAL LETTER TAR
1CA3; C; 10E3; # GEORGIAN MTAVRULI CAPITAL LETTER UN
1CA4; C; 10E4; # GEORGIAN MTAVRULI CAPITAL LETTER PHAR
1CA5; C; 10E5; # GEORGIAN MTAVRULI CAPITAL LETTER KHAR
1CA6; C; 10E6; # GEORGIAN MTAVRULI CAPITAL LETTER GHAN
1CA7; C; 10E7; # GEORGIAN MTAVRULI CAPITAL LETTER QAR
1CA8; C; 10E8; # GEORGIAN MTAVRULI CAPITAL LETTER SHIN
1CA9; C; 10E9; # GEORGIAN MTAVRULI CAPITAL LETTER CHIN
1CAA; C; 10EA; # GEORGIAN MTAVRULI CAPITAL LETTER CAN
1CAB; C; 10EB; # GEORGIAN MTAVRULI CAPITAL LETTER JIL
1CAC; C; 10EC; # GEORGIAN MTAVRULI CAPITAL LETTER CIL
1CAD; C; 10ED; # GEORGIAN MTAVRULI CAPITAL LETTER CHAR
1CAE; C; 10EE; # GEORGIAN MTAVRULI CAPITAL LETTER XAN
1CAF; C; 10EF; # GEORGIAN MTAVRULI CAPITAL LETTER JHAN
1CB0; C; 10F0; # GEORGIAN MTAVRULI CAPITAL LETTER HAE
1CB1; C; 10F1; # GEORGIAN MTAVRULI CAPITAL LETTER HE
1CB2; C; 10F2; # GEORGIAN MTAVRULI CAPITAL LETTER HIE
1CB3; C; 10F3; # GEORGIAN MTAVRULI CAPITAL LETTER WE
1CB4; C; 10F4; # GEORGIAN MTAVRULI CAPITAL LETTER HAR
1CB5; C; 10F5; # GEORGIAN MTAVRULI CAPITAL LETTER HOE
1CB6; C; 10F6; # GEORGIAN MTAVRULI CAPITAL LETTER FI
1CB7; C; 10F7; # GEORGIAN MTAVRULI CAPITAL LETTER YN
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | true |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/cache.py | youtube_dl/cache.py | # coding: utf-8
from __future__ import unicode_literals
import errno
import json
import os
import re
import shutil
import traceback
from .compat import (
compat_getenv,
compat_open as open,
compat_os_makedirs,
)
from .utils import (
error_to_compat_str,
escape_rfc3986,
expand_path,
is_outdated_version,
traverse_obj,
write_json_file,
)
from .version import __version__
class Cache(object):
_YTDL_DIR = 'youtube-dl'
_VERSION_KEY = _YTDL_DIR + '_version'
_DEFAULT_VERSION = '2021.12.17'
def __init__(self, ydl):
self._ydl = ydl
def _write_debug(self, *args, **kwargs):
self._ydl.write_debug(*args, **kwargs)
def _report_warning(self, *args, **kwargs):
self._ydl.report_warning(*args, **kwargs)
def _to_screen(self, *args, **kwargs):
self._ydl.to_screen(*args, **kwargs)
def _get_param(self, k, default=None):
return self._ydl.params.get(k, default)
def _get_root_dir(self):
res = self._get_param('cachedir')
if res is None:
cache_root = compat_getenv('XDG_CACHE_HOME', '~/.cache')
res = os.path.join(cache_root, self._YTDL_DIR)
return expand_path(res)
def _get_cache_fn(self, section, key, dtype):
assert re.match(r'^[\w.-]+$', section), \
'invalid section %r' % section
key = escape_rfc3986(key, safe='').replace('%', ',') # encode non-ascii characters
return os.path.join(
self._get_root_dir(), section, '%s.%s' % (key, dtype))
@property
def enabled(self):
return self._get_param('cachedir') is not False
def store(self, section, key, data, dtype='json'):
assert dtype in ('json',)
if not self.enabled:
return
fn = self._get_cache_fn(section, key, dtype)
try:
compat_os_makedirs(os.path.dirname(fn), exist_ok=True)
self._write_debug('Saving {section}.{key} to cache'.format(section=section, key=key))
write_json_file({self._VERSION_KEY: __version__, 'data': data}, fn)
except Exception:
tb = traceback.format_exc()
self._report_warning('Writing cache to {fn!r} failed: {tb}'.format(fn=fn, tb=tb))
def clear(self, section, key, dtype='json'):
if not self.enabled:
return
fn = self._get_cache_fn(section, key, dtype)
self._write_debug('Clearing {section}.{key} from cache'.format(section=section, key=key))
try:
os.remove(fn)
except Exception as e:
if getattr(e, 'errno') == errno.ENOENT:
# file not found
return
tb = traceback.format_exc()
self._report_warning('Clearing cache from {fn!r} failed: {tb}'.format(fn=fn, tb=tb))
def _validate(self, data, min_ver):
version = traverse_obj(data, self._VERSION_KEY)
if not version: # Backward compatibility
data, version = {'data': data}, self._DEFAULT_VERSION
if not is_outdated_version(version, min_ver or '0', assume_new=False):
return data['data']
self._write_debug('Discarding old cache from version {version} (needs {min_ver})'.format(version=version, min_ver=min_ver))
def load(self, section, key, dtype='json', default=None, **kw_min_ver):
assert dtype in ('json',)
min_ver = kw_min_ver.get('min_ver')
if not self.enabled:
return default
cache_fn = self._get_cache_fn(section, key, dtype)
try:
with open(cache_fn, encoding='utf-8') as cachef:
self._write_debug('Loading {section}.{key} from cache'.format(section=section, key=key), only_once=True)
return self._validate(json.load(cachef), min_ver)
except (ValueError, KeyError):
try:
file_size = 'size: %d' % os.path.getsize(cache_fn)
except (OSError, IOError) as oe:
file_size = error_to_compat_str(oe)
self._report_warning('Cache retrieval from %s failed (%s)' % (cache_fn, file_size))
except Exception as e:
if getattr(e, 'errno') == errno.ENOENT:
# no cache available
return
self._report_warning('Cache retrieval from %s failed' % (cache_fn,))
return default
def remove(self):
if not self.enabled:
self._to_screen('Cache is disabled (Did you combine --no-cache-dir and --rm-cache-dir?)')
return
cachedir = self._get_root_dir()
if not any((term in cachedir) for term in ('cache', 'tmp')):
raise Exception('Not removing directory %s - this does not look like a cache dir' % (cachedir,))
self._to_screen(
'Removing cache dir %s .' % (cachedir,), skip_eol=True, ),
if os.path.exists(cachedir):
self._to_screen('.', skip_eol=True)
shutil.rmtree(cachedir)
self._to_screen('.')
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/traversal.py | youtube_dl/traversal.py | # coding: utf-8
# TODO: move these utils.fns here and move import to utils
# flake8: noqa
from .utils import (
dict_get,
get_first,
require,
subs_list_to_dict,
T,
traverse_obj,
unpack,
value,
)
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/options.py | youtube_dl/options.py | from __future__ import unicode_literals
import os.path
import optparse
import re
import sys
from .downloader.external import list_external_downloaders
from .compat import (
compat_expanduser,
compat_get_terminal_size,
compat_getenv,
compat_kwargs,
compat_open as open,
compat_shlex_split,
)
from .utils import (
preferredencoding,
write_string,
)
from .version import __version__
def _hide_login_info(opts):
PRIVATE_OPTS = set(['-p', '--password', '-u', '--username', '--video-password', '--ap-password', '--ap-username'])
eqre = re.compile('^(?P<key>' + ('|'.join(re.escape(po) for po in PRIVATE_OPTS)) + ')=.+$')
def _scrub_eq(o):
m = eqre.match(o)
if m:
return m.group('key') + '=PRIVATE'
else:
return o
opts = list(map(_scrub_eq, opts))
for idx, opt in enumerate(opts):
if opt in PRIVATE_OPTS and idx + 1 < len(opts):
opts[idx + 1] = 'PRIVATE'
return opts
def parseOpts(overrideArguments=None):
def _readOptions(filename_bytes, default=[]):
try:
optionf = open(filename_bytes, encoding=preferredencoding())
except IOError:
return default # silently skip if file is not present
try:
contents = optionf.read()
res = compat_shlex_split(contents, comments=True)
finally:
optionf.close()
return res
def _readUserConf():
xdg_config_home = compat_getenv('XDG_CONFIG_HOME')
if xdg_config_home:
userConfFile = os.path.join(xdg_config_home, 'youtube-dl', 'config')
if not os.path.isfile(userConfFile):
userConfFile = os.path.join(xdg_config_home, 'youtube-dl.conf')
else:
userConfFile = os.path.join(compat_expanduser('~'), '.config', 'youtube-dl', 'config')
if not os.path.isfile(userConfFile):
userConfFile = os.path.join(compat_expanduser('~'), '.config', 'youtube-dl.conf')
userConf = _readOptions(userConfFile, None)
if userConf is None:
appdata_dir = compat_getenv('appdata')
if appdata_dir:
userConf = _readOptions(
os.path.join(appdata_dir, 'youtube-dl', 'config'),
default=None)
if userConf is None:
userConf = _readOptions(
os.path.join(appdata_dir, 'youtube-dl', 'config.txt'),
default=None)
if userConf is None:
userConf = _readOptions(
os.path.join(compat_expanduser('~'), 'youtube-dl.conf'),
default=None)
if userConf is None:
userConf = _readOptions(
os.path.join(compat_expanduser('~'), 'youtube-dl.conf.txt'),
default=None)
if userConf is None:
userConf = []
return userConf
def _format_option_string(option):
''' ('-o', '--option') -> -o, --format METAVAR'''
opts = []
if option._short_opts:
opts.append(option._short_opts[0])
if option._long_opts:
opts.append(option._long_opts[0])
if len(opts) > 1:
opts.insert(1, ', ')
if option.takes_value():
opts.append(' %s' % option.metavar)
return ''.join(opts)
def _comma_separated_values_options_callback(option, opt_str, value, parser):
setattr(parser.values, option.dest, value.split(','))
# No need to wrap help messages if we're on a wide console
columns = compat_get_terminal_size().columns
max_width = columns if columns else 80
max_help_position = 80
fmt = optparse.IndentedHelpFormatter(width=max_width, max_help_position=max_help_position)
fmt.format_option_strings = _format_option_string
kw = {
'version': __version__,
'formatter': fmt,
'usage': '%prog [OPTIONS] URL [URL...]',
'conflict_handler': 'resolve',
}
parser = optparse.OptionParser(**compat_kwargs(kw))
general = optparse.OptionGroup(parser, 'General Options')
general.add_option(
'-h', '--help',
action='help',
help='Print this help text and exit')
general.add_option(
'--version',
action='version',
help='Print program version and exit')
general.add_option(
'-U', '--update',
action='store_true', dest='update_self',
help='Update this program to latest version. Make sure that you have sufficient permissions (run with sudo if needed)')
general.add_option(
'-i', '--ignore-errors',
action='store_true', dest='ignoreerrors', default=False,
help='Continue on download errors, for example to skip unavailable videos in a playlist')
general.add_option(
'--abort-on-error',
action='store_false', dest='ignoreerrors',
help='Abort downloading of further videos (in the playlist or the command line) if an error occurs')
general.add_option(
'--dump-user-agent',
action='store_true', dest='dump_user_agent', default=False,
help='Display the current browser identification')
general.add_option(
'--list-extractors',
action='store_true', dest='list_extractors', default=False,
help='List all supported extractors')
general.add_option(
'--extractor-descriptions',
action='store_true', dest='list_extractor_descriptions', default=False,
help='Output descriptions of all supported extractors')
general.add_option(
'--force-generic-extractor',
action='store_true', dest='force_generic_extractor', default=False,
help='Force extraction to use the generic extractor')
general.add_option(
'--default-search',
dest='default_search', metavar='PREFIX',
help='Use this prefix for unqualified URLs. For example "gvsearch2:" downloads two videos from google videos for youtube-dl "large apple". Use the value "auto" to let youtube-dl guess ("auto_warning" to emit a warning when guessing). "error" just throws an error. The default value "fixup_error" repairs broken URLs, but emits an error if this is not possible instead of searching.')
general.add_option(
'--ignore-config',
action='store_true',
help='Do not read configuration files. '
'When given in the global configuration file /etc/youtube-dl.conf: '
'Do not read the user configuration in ~/.config/youtube-dl/config '
'(%APPDATA%/youtube-dl/config.txt on Windows)')
general.add_option(
'--config-location',
dest='config_location', metavar='PATH',
help='Location of the configuration file; either the path to the config or its containing directory.')
general.add_option(
'--flat-playlist',
action='store_const', dest='extract_flat', const='in_playlist',
default=False,
help='Do not extract the videos of a playlist, only list them.')
general.add_option(
'--mark-watched',
action='store_true', dest='mark_watched', default=False,
help='Mark videos watched (YouTube only)')
general.add_option(
'--no-mark-watched',
action='store_false', dest='mark_watched', default=False,
help='Do not mark videos watched (YouTube only)')
general.add_option(
'--no-color', '--no-colors',
action='store_true', dest='no_color',
default=False,
help='Do not emit color codes in output')
network = optparse.OptionGroup(parser, 'Network Options')
network.add_option(
'--proxy', dest='proxy',
default=None, metavar='URL',
help='Use the specified HTTP/HTTPS/SOCKS proxy. To enable '
'SOCKS proxy, specify a proper scheme. For example '
'socks5://127.0.0.1:1080/. Pass in an empty string (--proxy "") '
'for direct connection')
network.add_option(
'--socket-timeout',
dest='socket_timeout', type=float, default=None, metavar='SECONDS',
help='Time to wait before giving up, in seconds')
network.add_option(
'--source-address',
metavar='IP', dest='source_address', default=None,
help='Client-side IP address to bind to',
)
network.add_option(
'-4', '--force-ipv4',
action='store_const', const='0.0.0.0', dest='source_address',
help='Make all connections via IPv4',
)
network.add_option(
'-6', '--force-ipv6',
action='store_const', const='::', dest='source_address',
help='Make all connections via IPv6',
)
geo = optparse.OptionGroup(parser, 'Geo Restriction')
geo.add_option(
'--geo-verification-proxy',
dest='geo_verification_proxy', default=None, metavar='URL',
help='Use this proxy to verify the IP address for some geo-restricted sites. '
'The default proxy specified by --proxy (or none, if the option is not present) is used for the actual downloading.')
geo.add_option(
'--cn-verification-proxy',
dest='cn_verification_proxy', default=None, metavar='URL',
help=optparse.SUPPRESS_HELP)
geo.add_option(
'--geo-bypass',
action='store_true', dest='geo_bypass', default=True,
help='Bypass geographic restriction via faking X-Forwarded-For HTTP header')
geo.add_option(
'--no-geo-bypass',
action='store_false', dest='geo_bypass', default=True,
help='Do not bypass geographic restriction via faking X-Forwarded-For HTTP header')
geo.add_option(
'--geo-bypass-country', metavar='CODE',
dest='geo_bypass_country', default=None,
help='Force bypass geographic restriction with explicitly provided two-letter ISO 3166-2 country code')
geo.add_option(
'--geo-bypass-ip-block', metavar='IP_BLOCK',
dest='geo_bypass_ip_block', default=None,
help='Force bypass geographic restriction with explicitly provided IP block in CIDR notation')
selection = optparse.OptionGroup(parser, 'Video Selection')
selection.add_option(
'--playlist-start',
dest='playliststart', metavar='NUMBER', default=1, type=int,
help='Playlist video to start at (default is %default)')
selection.add_option(
'--playlist-end',
dest='playlistend', metavar='NUMBER', default=None, type=int,
help='Playlist video to end at (default is last)')
selection.add_option(
'--playlist-items',
dest='playlist_items', metavar='ITEM_SPEC', default=None,
help='Playlist video items to download. Specify indices of the videos in the playlist separated by commas like: "--playlist-items 1,2,5,8" if you want to download videos indexed 1, 2, 5, 8 in the playlist. You can specify range: "--playlist-items 1-3,7,10-13", it will download the videos at index 1, 2, 3, 7, 10, 11, 12 and 13.')
selection.add_option(
'--match-title',
dest='matchtitle', metavar='REGEX',
help='Download only matching titles (case-insensitive regex or alphanumeric sub-string)')
selection.add_option(
'--reject-title',
dest='rejecttitle', metavar='REGEX',
help='Skip download for matching titles (case-insensitive regex or alphanumeric sub-string)')
selection.add_option(
'--max-downloads',
dest='max_downloads', metavar='NUMBER', type=int, default=None,
help='Abort after downloading NUMBER files')
selection.add_option(
'--min-filesize',
metavar='SIZE', dest='min_filesize', default=None,
help='Do not download any videos smaller than SIZE (e.g. 50k or 44.6m)')
selection.add_option(
'--max-filesize',
metavar='SIZE', dest='max_filesize', default=None,
help='Do not download any videos larger than SIZE (e.g. 50k or 44.6m)')
selection.add_option(
'--date',
metavar='DATE', dest='date', default=None,
help='Download only videos uploaded in this date')
selection.add_option(
'--datebefore',
metavar='DATE', dest='datebefore', default=None,
help='Download only videos uploaded on or before this date (i.e. inclusive)')
selection.add_option(
'--dateafter',
metavar='DATE', dest='dateafter', default=None,
help='Download only videos uploaded on or after this date (i.e. inclusive)')
selection.add_option(
'--min-views',
metavar='COUNT', dest='min_views', default=None, type=int,
help='Do not download any videos with less than COUNT views')
selection.add_option(
'--max-views',
metavar='COUNT', dest='max_views', default=None, type=int,
help='Do not download any videos with more than COUNT views')
selection.add_option(
'--match-filter',
metavar='FILTER', dest='match_filter', default=None,
help=(
'Generic video filter. '
'Specify any key (see the "OUTPUT TEMPLATE" for a list of available keys) to '
'match if the key is present, '
'!key to check if the key is not present, '
'key > NUMBER (like "comment_count > 12", also works with '
'>=, <, <=, !=, =) to compare against a number, '
'key = \'LITERAL\' (like "uploader = \'Mike Smith\'", also works with !=) '
'to match against a string literal '
'and & to require multiple matches. '
'Values which are not known are excluded unless you '
'put a question mark (?) after the operator. '
'For example, to only match videos that have been liked more than '
'100 times and disliked less than 50 times (or the dislike '
'functionality is not available at the given service), but who '
'also have a description, use --match-filter '
'"like_count > 100 & dislike_count <? 50 & description" .'
))
selection.add_option(
'--no-playlist',
action='store_true', dest='noplaylist', default=False,
help='Download only the video, if the URL refers to a video and a playlist.')
selection.add_option(
'--yes-playlist',
action='store_false', dest='noplaylist', default=False,
help='Download the playlist, if the URL refers to a video and a playlist.')
selection.add_option(
'--age-limit',
metavar='YEARS', dest='age_limit', default=None, type=int,
help='Download only videos suitable for the given age')
selection.add_option(
'--download-archive', metavar='FILE',
dest='download_archive',
help='Download only videos not listed in the archive file. Record the IDs of all downloaded videos in it.')
selection.add_option(
'--include-ads',
dest='include_ads', action='store_true',
help='Download advertisements as well (experimental)')
authentication = optparse.OptionGroup(parser, 'Authentication Options')
authentication.add_option(
'-u', '--username',
dest='username', metavar='USERNAME',
help='Login with this account ID')
authentication.add_option(
'-p', '--password',
dest='password', metavar='PASSWORD',
help='Account password. If this option is left out, youtube-dl will ask interactively.')
authentication.add_option(
'-2', '--twofactor',
dest='twofactor', metavar='TWOFACTOR',
help='Two-factor authentication code')
authentication.add_option(
'-n', '--netrc',
action='store_true', dest='usenetrc', default=False,
help='Use .netrc authentication data')
authentication.add_option(
'--video-password',
dest='videopassword', metavar='PASSWORD',
help='Video password (vimeo, youku)')
adobe_pass = optparse.OptionGroup(parser, 'Adobe Pass Options')
adobe_pass.add_option(
'--ap-mso',
dest='ap_mso', metavar='MSO',
help='Adobe Pass multiple-system operator (TV provider) identifier, use --ap-list-mso for a list of available MSOs')
adobe_pass.add_option(
'--ap-username',
dest='ap_username', metavar='USERNAME',
help='Multiple-system operator account login')
adobe_pass.add_option(
'--ap-password',
dest='ap_password', metavar='PASSWORD',
help='Multiple-system operator account password. If this option is left out, youtube-dl will ask interactively.')
adobe_pass.add_option(
'--ap-list-mso',
action='store_true', dest='ap_list_mso', default=False,
help='List all supported multiple-system operators')
video_format = optparse.OptionGroup(parser, 'Video Format Options')
video_format.add_option(
'-f', '--format',
action='store', dest='format', metavar='FORMAT', default=None,
help='Video format code, see the "FORMAT SELECTION" for all the info')
video_format.add_option(
'--all-formats',
action='store_const', dest='format', const='all',
help='Download all available video formats')
video_format.add_option(
'--prefer-free-formats',
action='store_true', dest='prefer_free_formats', default=False,
help='Prefer free video formats unless a specific one is requested')
video_format.add_option(
'-F', '--list-formats',
action='store_true', dest='listformats',
help='List all available formats of requested videos')
video_format.add_option(
'--no-list-formats',
action='store_false', dest='listformats',
help='Do not list available formats of requested videos (default)')
video_format.add_option(
'--youtube-include-dash-manifest',
action='store_true', dest='youtube_include_dash_manifest', default=True,
help=optparse.SUPPRESS_HELP)
video_format.add_option(
'--youtube-skip-dash-manifest',
action='store_false', dest='youtube_include_dash_manifest',
help='Do not download the DASH manifests and related data on YouTube videos')
video_format.add_option(
'--youtube-player-js-variant',
action='store', dest='youtube_player_js_variant',
help='For YouTube, the player javascript variant to use for n/sig deciphering; `actual` to follow the site; default `%default`.',
choices=('actual', 'main', 'tcc', 'tce', 'es5', 'es6', 'tv', 'tv_es6', 'phone', 'tablet'),
default='actual', metavar='VARIANT')
video_format.add_option(
'--youtube-player-js-version',
action='store', dest='youtube_player_js_version',
help='For YouTube, the player javascript version to use for n/sig deciphering, specified as `signature_timestamp@hash`, or `actual` to follow the site; default `%default`',
default='actual', metavar='STS@HASH')
video_format.add_option(
'--merge-output-format',
action='store', dest='merge_output_format', metavar='FORMAT', default=None,
help=(
'If a merge is required (e.g. bestvideo+bestaudio), '
'output to given container format. One of mkv, mp4, ogg, webm, flv. '
'Ignored if no merge is required'))
subtitles = optparse.OptionGroup(parser, 'Subtitle Options')
subtitles.add_option(
'--write-sub', '--write-srt',
action='store_true', dest='writesubtitles', default=False,
help='Write subtitle file')
subtitles.add_option(
'--write-auto-sub', '--write-automatic-sub',
action='store_true', dest='writeautomaticsub', default=False,
help='Write automatically generated subtitle file (YouTube only)')
subtitles.add_option(
'--all-subs',
action='store_true', dest='allsubtitles', default=False,
help='Download all the available subtitles of the video')
subtitles.add_option(
'--list-subs',
action='store_true', dest='listsubtitles', default=False,
help='List all available subtitles for the video')
subtitles.add_option(
'--sub-format',
action='store', dest='subtitlesformat', metavar='FORMAT', default='best',
help='Subtitle format, accepts formats preference, for example: "srt" or "ass/srt/best"')
subtitles.add_option(
'--sub-lang', '--sub-langs', '--srt-lang',
action='callback', dest='subtitleslangs', metavar='LANGS', type='str',
default=[], callback=_comma_separated_values_options_callback,
help='Languages of the subtitles to download (optional) separated by commas, use --list-subs for available language tags')
downloader = optparse.OptionGroup(parser, 'Download Options')
downloader.add_option(
'-r', '--limit-rate', '--rate-limit',
dest='ratelimit', metavar='RATE',
help='Maximum download rate in bytes per second (e.g. 50K or 4.2M)')
downloader.add_option(
'-R', '--retries',
dest='retries', metavar='RETRIES', default=10,
help='Number of retries (default is %default), or "infinite".')
downloader.add_option(
'--fragment-retries',
dest='fragment_retries', metavar='RETRIES', default=10,
help='Number of retries for a fragment (default is %default), or "infinite" (DASH, hlsnative and ISM)')
downloader.add_option(
'--skip-unavailable-fragments',
action='store_true', dest='skip_unavailable_fragments', default=True,
help='Skip unavailable fragments (DASH, hlsnative and ISM)')
downloader.add_option(
'--abort-on-unavailable-fragment',
action='store_false', dest='skip_unavailable_fragments',
help='Abort downloading when some fragment is not available')
downloader.add_option(
'--keep-fragments',
action='store_true', dest='keep_fragments', default=False,
help='Keep downloaded fragments on disk after downloading is finished; fragments are erased by default')
downloader.add_option(
'--buffer-size',
dest='buffersize', metavar='SIZE', default='1024',
help='Size of download buffer (e.g. 1024 or 16K) (default is %default)')
downloader.add_option(
'--no-resize-buffer',
action='store_true', dest='noresizebuffer', default=False,
help='Do not automatically adjust the buffer size. By default, the buffer size is automatically resized from an initial value of SIZE.')
downloader.add_option(
'--http-chunk-size',
dest='http_chunk_size', metavar='SIZE', default=None,
help='Size of a chunk for chunk-based HTTP downloading (e.g. 10485760 or 10M) (default is disabled). '
'May be useful for bypassing bandwidth throttling imposed by a webserver (experimental)')
downloader.add_option(
'--test',
action='store_true', dest='test', default=False,
help=optparse.SUPPRESS_HELP)
downloader.add_option(
'--playlist-reverse',
action='store_true',
help='Download playlist videos in reverse order')
downloader.add_option(
'--playlist-random',
action='store_true',
help='Download playlist videos in random order')
downloader.add_option(
'--xattr-set-filesize',
dest='xattr_set_filesize', action='store_true',
help='Set file xattribute ytdl.filesize with expected file size')
downloader.add_option(
'--hls-prefer-native',
dest='hls_prefer_native', action='store_true', default=None,
help='Use the native HLS downloader instead of ffmpeg')
downloader.add_option(
'--hls-prefer-ffmpeg',
dest='hls_prefer_native', action='store_false', default=None,
help='Use ffmpeg instead of the native HLS downloader')
downloader.add_option(
'--hls-use-mpegts',
dest='hls_use_mpegts', action='store_true',
help='Use the mpegts container for HLS videos, allowing to play the '
'video while downloading (some players may not be able to play it)')
downloader.add_option(
'--external-downloader',
dest='external_downloader', metavar='COMMAND',
help='Use the specified external downloader. '
'Currently supports %s' % ','.join(list_external_downloaders()))
downloader.add_option(
'--external-downloader-args',
dest='external_downloader_args', metavar='ARGS',
help='Give these arguments to the external downloader')
workarounds = optparse.OptionGroup(parser, 'Workarounds')
workarounds.add_option(
'--encoding',
dest='encoding', metavar='ENCODING',
help='Force the specified encoding (experimental)')
workarounds.add_option(
'--no-check-certificate',
action='store_true', dest='no_check_certificate', default=False,
help='Suppress HTTPS certificate validation')
workarounds.add_option(
'--no-check-extensions',
action='store_true', dest='no_check_extensions', default=False,
help='Suppress file extension validation')
workarounds.add_option(
'--prefer-insecure',
'--prefer-unsecure', action='store_true', dest='prefer_insecure',
help='Use an unencrypted connection to retrieve information about the video. (Currently supported only for YouTube)')
workarounds.add_option(
'--user-agent',
metavar='UA', dest='user_agent',
help='Specify a custom user agent')
workarounds.add_option(
'--referer',
metavar='URL', dest='referer', default=None,
help='Specify a custom Referer: use if the video access is restricted to one domain',
)
workarounds.add_option(
'--add-header',
metavar='FIELD:VALUE', dest='headers', action='append',
help=('Specify a custom HTTP header and its value, separated by a colon \':\'. You can use this option multiple times. '
'NB Use --cookies rather than adding a Cookie header if its contents may be sensitive; '
'data from a Cookie header will be sent to all domains, not just the one intended')
)
workarounds.add_option(
'--bidi-workaround',
dest='bidi_workaround', action='store_true',
help='Work around terminals that lack bidirectional text support. Requires bidiv or fribidi executable in PATH')
workarounds.add_option(
'--sleep-interval', '--min-sleep-interval', metavar='SECONDS',
dest='sleep_interval', type=float,
help=(
'Number of seconds to sleep before each download when used alone '
'or a lower bound of a range for randomized sleep before each download '
'(minimum possible number of seconds to sleep) when used along with '
'--max-sleep-interval.'))
workarounds.add_option(
'--max-sleep-interval', metavar='SECONDS',
dest='max_sleep_interval', type=float,
help=(
'Upper bound of a range for randomized sleep before each download '
'(maximum possible number of seconds to sleep). Must only be used '
'along with --min-sleep-interval.'))
verbosity = optparse.OptionGroup(parser, 'Verbosity / Simulation Options')
verbosity.add_option(
'-q', '--quiet',
action='store_true', dest='quiet', default=False,
help='Activate quiet mode')
verbosity.add_option(
'--no-warnings',
dest='no_warnings', action='store_true', default=False,
help='Ignore warnings')
verbosity.add_option(
'-s', '--simulate',
action='store_true', dest='simulate', default=False,
help='Do not download the video and do not write anything to disk')
verbosity.add_option(
'--skip-download',
action='store_true', dest='skip_download', default=False,
help='Do not download the video')
verbosity.add_option(
'-g', '--get-url',
action='store_true', dest='geturl', default=False,
help='Simulate, quiet but print URL')
verbosity.add_option(
'-e', '--get-title',
action='store_true', dest='gettitle', default=False,
help='Simulate, quiet but print title')
verbosity.add_option(
'--get-id',
action='store_true', dest='getid', default=False,
help='Simulate, quiet but print id')
verbosity.add_option(
'--get-thumbnail',
action='store_true', dest='getthumbnail', default=False,
help='Simulate, quiet but print thumbnail URL')
verbosity.add_option(
'--get-description',
action='store_true', dest='getdescription', default=False,
help='Simulate, quiet but print video description')
verbosity.add_option(
'--get-duration',
action='store_true', dest='getduration', default=False,
help='Simulate, quiet but print video length')
verbosity.add_option(
'--get-filename',
action='store_true', dest='getfilename', default=False,
help='Simulate, quiet but print output filename')
verbosity.add_option(
'--get-format',
action='store_true', dest='getformat', default=False,
help='Simulate, quiet but print output format')
verbosity.add_option(
'-j', '--dump-json',
action='store_true', dest='dumpjson', default=False,
help='Simulate, quiet but print JSON information. See the "OUTPUT TEMPLATE" for a description of available keys.')
verbosity.add_option(
'-J', '--dump-single-json',
action='store_true', dest='dump_single_json', default=False,
help='Simulate, quiet but print JSON information for each command-line argument. If the URL refers to a playlist, dump the whole playlist information in a single line.')
verbosity.add_option(
'--print-json',
action='store_true', dest='print_json', default=False,
help='Be quiet and print the video information as JSON (video is still being downloaded).',
)
verbosity.add_option(
'--newline',
action='store_true', dest='progress_with_newline', default=False,
help='Output progress bar as new lines')
verbosity.add_option(
'--no-progress',
action='store_true', dest='noprogress', default=False,
help='Do not print progress bar')
verbosity.add_option(
'--console-title',
action='store_true', dest='consoletitle', default=False,
help='Display progress in console titlebar')
verbosity.add_option(
'-v', '--verbose',
action='store_true', dest='verbose', default=False,
help='Print various debugging information')
verbosity.add_option(
'--dump-pages', '--dump-intermediate-pages',
action='store_true', dest='dump_intermediate_pages', default=False,
help='Print downloaded pages encoded using base64 to debug problems (very verbose)')
verbosity.add_option(
'--write-pages',
action='store_true', dest='write_pages', default=False,
help='Write downloaded intermediary pages to files in the current directory to debug problems')
verbosity.add_option(
'--youtube-print-sig-code',
action='store_true', dest='youtube_print_sig_code', default=False,
help=optparse.SUPPRESS_HELP)
verbosity.add_option(
'--print-traffic', '--dump-headers',
dest='debug_printtraffic', action='store_true', default=False,
help='Display sent and read HTTP traffic')
verbosity.add_option(
'-C', '--call-home',
dest='call_home', action='store_true', default=False,
help='Contact the youtube-dl server for debugging')
verbosity.add_option(
'--no-call-home',
dest='call_home', action='store_false', default=False,
help='Do NOT contact the youtube-dl server for debugging')
filesystem = optparse.OptionGroup(parser, 'Filesystem Options')
filesystem.add_option(
'-a', '--batch-file',
dest='batchfile', metavar='FILE',
help="File containing URLs to download ('-' for stdin), one URL per line. "
"Lines starting with '#', ';' or ']' are considered as comments and ignored.")
filesystem.add_option(
'--id', default=False,
action='store_true', dest='useid', help='Use only video ID in file name')
filesystem.add_option(
'-o', '--output',
dest='outtmpl', metavar='TEMPLATE',
help=('Output filename template, see the "OUTPUT TEMPLATE" for all the info'))
filesystem.add_option(
'--output-na-placeholder',
dest='outtmpl_na_placeholder', metavar='PLACEHOLDER', default='NA',
help=('Placeholder value for unavailable meta fields in output filename template (default is "%default")'))
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | true |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/odnoklassniki.py | youtube_dl/extractor/odnoklassniki.py | # coding: utf-8
from __future__ import unicode_literals
import re
from .common import InfoExtractor
from ..compat import (
compat_etree_fromstring,
compat_parse_qs,
compat_urllib_parse_unquote,
compat_urllib_parse_urlparse,
)
from ..utils import (
ExtractorError,
unified_strdate,
int_or_none,
qualities,
unescapeHTML,
urlencode_postdata,
)
class OdnoklassnikiIE(InfoExtractor):
_VALID_URL = r'''(?x)
https?://
(?:(?:www|m|mobile)\.)?
(?:odnoklassniki|ok)\.ru/
(?:
video(?:embed)?/|
web-api/video/moviePlayer/|
live/|
dk\?.*?st\.mvId=
)
(?P<id>[\d-]+)
'''
_TESTS = [{
# metadata in JSON
'url': 'http://ok.ru/video/20079905452',
'md5': '0b62089b479e06681abaaca9d204f152',
'info_dict': {
'id': '20079905452',
'ext': 'mp4',
'title': 'Культура меняет нас (прекрасный ролик!))',
'duration': 100,
'upload_date': '20141207',
'uploader_id': '330537914540',
'uploader': 'Виталий Добровольский',
'like_count': int,
'age_limit': 0,
},
}, {
# metadataUrl
'url': 'http://ok.ru/video/63567059965189-0?fromTime=5',
'md5': '6ff470ea2dd51d5d18c295a355b0b6bc',
'info_dict': {
'id': '63567059965189-0',
'ext': 'mp4',
'title': 'Девушка без комплексов ...',
'duration': 191,
'upload_date': '20150518',
'uploader_id': '534380003155',
'uploader': '☭ Андрей Мещанинов ☭',
'like_count': int,
'age_limit': 0,
'start_time': 5,
},
}, {
# YouTube embed (metadataUrl, provider == USER_YOUTUBE)
'url': 'http://ok.ru/video/64211978996595-1',
'md5': '2f206894ffb5dbfcce2c5a14b909eea5',
'info_dict': {
'id': 'V_VztHT5BzY',
'ext': 'mp4',
'title': 'Космическая среда от 26 августа 2015',
'description': 'md5:848eb8b85e5e3471a3a803dae1343ed0',
'duration': 440,
'upload_date': '20150826',
'uploader_id': 'tvroscosmos',
'uploader': 'Телестудия Роскосмоса',
'age_limit': 0,
},
}, {
# YouTube embed (metadata, provider == USER_YOUTUBE, no metadata.movie.title field)
'url': 'http://ok.ru/video/62036049272859-0',
'info_dict': {
'id': '62036049272859-0',
'ext': 'mp4',
'title': 'МУЗЫКА ДОЖДЯ .',
'description': 'md5:6f1867132bd96e33bf53eda1091e8ed0',
'upload_date': '20120106',
'uploader_id': '473534735899',
'uploader': 'МARINA D',
'age_limit': 0,
},
'params': {
'skip_download': True,
},
'skip': 'Video has not been found',
}, {
'url': 'http://ok.ru/web-api/video/moviePlayer/20079905452',
'only_matching': True,
}, {
'url': 'http://www.ok.ru/video/20648036891',
'only_matching': True,
}, {
'url': 'http://www.ok.ru/videoembed/20648036891',
'only_matching': True,
}, {
'url': 'http://m.ok.ru/video/20079905452',
'only_matching': True,
}, {
'url': 'http://mobile.ok.ru/video/20079905452',
'only_matching': True,
}, {
'url': 'https://www.ok.ru/live/484531969818',
'only_matching': True,
}, {
'url': 'https://m.ok.ru/dk?st.cmd=movieLayer&st.discId=863789452017&st.retLoc=friend&st.rtu=%2Fdk%3Fst.cmd%3DfriendMovies%26st.mode%3Down%26st.mrkId%3D%257B%2522uploadedMovieMarker%2522%253A%257B%2522marker%2522%253A%25221519410114503%2522%252C%2522hasMore%2522%253Atrue%257D%252C%2522sharedMovieMarker%2522%253A%257B%2522marker%2522%253Anull%252C%2522hasMore%2522%253Afalse%257D%257D%26st.friendId%3D561722190321%26st.frwd%3Don%26_prevCmd%3DfriendMovies%26tkn%3D7257&st.discType=MOVIE&st.mvId=863789452017&_prevCmd=friendMovies&tkn=3648#lst#',
'only_matching': True,
}, {
# Paid video
'url': 'https://ok.ru/video/954886983203',
'only_matching': True,
}]
@staticmethod
def _extract_url(webpage):
mobj = re.search(
r'<iframe[^>]+src=(["\'])(?P<url>(?:https?:)?//(?:odnoklassniki|ok)\.ru/videoembed/.+?)\1', webpage)
if mobj:
return mobj.group('url')
def _real_extract(self, url):
start_time = int_or_none(compat_parse_qs(
compat_urllib_parse_urlparse(url).query).get('fromTime', [None])[0])
video_id = self._match_id(url)
webpage = self._download_webpage(
'http://ok.ru/video/%s' % video_id, video_id)
error = self._search_regex(
r'[^>]+class="vp_video_stub_txt"[^>]*>([^<]+)<',
webpage, 'error', default=None)
if error:
raise ExtractorError(error, expected=True)
player = self._parse_json(
unescapeHTML(self._search_regex(
r'data-options=(?P<quote>["\'])(?P<player>{.+?%s.+?})(?P=quote)' % video_id,
webpage, 'player', group='player')),
video_id)
flashvars = player['flashvars']
metadata = flashvars.get('metadata')
if metadata:
metadata = self._parse_json(metadata, video_id)
else:
data = {}
st_location = flashvars.get('location')
if st_location:
data['st.location'] = st_location
metadata = self._download_json(
compat_urllib_parse_unquote(flashvars['metadataUrl']),
video_id, 'Downloading metadata JSON',
data=urlencode_postdata(data))
movie = metadata['movie']
# Some embedded videos may not contain title in movie dict (e.g.
# http://ok.ru/video/62036049272859-0) thus we allow missing title
# here and it's going to be extracted later by an extractor that
# will process the actual embed.
provider = metadata.get('provider')
title = movie['title'] if provider == 'UPLOADED_ODKL' else movie.get('title')
thumbnail = movie.get('poster')
duration = int_or_none(movie.get('duration'))
author = metadata.get('author', {})
uploader_id = author.get('id')
uploader = author.get('name')
upload_date = unified_strdate(self._html_search_meta(
'ya:ovs:upload_date', webpage, 'upload date', default=None))
age_limit = None
adult = self._html_search_meta(
'ya:ovs:adult', webpage, 'age limit', default=None)
if adult:
age_limit = 18 if adult == 'true' else 0
like_count = int_or_none(metadata.get('likeCount'))
info = {
'id': video_id,
'title': title,
'thumbnail': thumbnail,
'duration': duration,
'upload_date': upload_date,
'uploader': uploader,
'uploader_id': uploader_id,
'like_count': like_count,
'age_limit': age_limit,
'start_time': start_time,
}
if provider == 'USER_YOUTUBE':
info.update({
'_type': 'url_transparent',
'url': movie['contentId'],
})
return info
assert title
if provider == 'LIVE_TV_APP':
info['title'] = self._live_title(title)
quality = qualities(('4', '0', '1', '2', '3', '5'))
formats = [{
'url': f['url'],
'ext': 'mp4',
'format_id': f['name'],
} for f in metadata['videos']]
m3u8_url = metadata.get('hlsManifestUrl')
if m3u8_url:
formats.extend(self._extract_m3u8_formats(
m3u8_url, video_id, 'mp4', 'm3u8_native',
m3u8_id='hls', fatal=False))
dash_manifest = metadata.get('metadataEmbedded')
if dash_manifest:
formats.extend(self._parse_mpd_formats(
compat_etree_fromstring(dash_manifest), 'mpd'))
for fmt in formats:
fmt_type = self._search_regex(
r'\btype[/=](\d)', fmt['url'],
'format type', default=None)
if fmt_type:
fmt['quality'] = quality(fmt_type)
# Live formats
m3u8_url = metadata.get('hlsMasterPlaylistUrl')
if m3u8_url:
formats.extend(self._extract_m3u8_formats(
m3u8_url, video_id, 'mp4', entry_protocol='m3u8',
m3u8_id='hls', fatal=False))
rtmp_url = metadata.get('rtmpUrl')
if rtmp_url:
formats.append({
'url': rtmp_url,
'format_id': 'rtmp',
'ext': 'flv',
})
if not formats:
payment_info = metadata.get('paymentInfo')
if payment_info:
raise ExtractorError('This video is paid, subscribe to download it', expected=True)
self._sort_formats(formats)
info['formats'] = formats
return info
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/thisvid.py | youtube_dl/extractor/thisvid.py | # coding: utf-8
from __future__ import unicode_literals
import re
import itertools
from .common import InfoExtractor
from ..compat import (
compat_urlparse,
)
from ..utils import (
clean_html,
get_element_by_class,
int_or_none,
merge_dicts,
url_or_none,
urljoin,
)
class ThisVidIE(InfoExtractor):
_VALID_URL = r'https?://(?:www\.)?thisvid\.com/(?P<type>videos|embed)/(?P<id>[A-Za-z0-9-]+)'
_TESTS = [{
'url': 'https://thisvid.com/videos/sitting-on-ball-tight-jeans/',
'md5': '839becb572995687e11a69dc4358a386',
'info_dict': {
'id': '3533241',
'ext': 'mp4',
'title': 'Sitting on ball tight jeans',
'description': 'md5:372353bb995883d1b65fddf507489acd',
'thumbnail': r're:https?://\w+\.thisvid\.com/(?:[^/]+/)+3533241/preview\.jpg',
'uploader_id': '150629',
'uploader': 'jeanslevisjeans',
'age_limit': 18,
}
}, {
'url': 'https://thisvid.com/embed/3533241/',
'md5': '839becb572995687e11a69dc4358a386',
'info_dict': {
'id': '3533241',
'ext': 'mp4',
'title': 'Sitting on ball tight jeans',
'thumbnail': r're:https?://\w+\.thisvid\.com/(?:[^/]+/)+3533241/preview\.jpg',
'uploader_id': '150629',
'uploader': 'jeanslevisjeans',
'age_limit': 18,
}
}]
def _real_extract(self, url):
main_id, type_ = re.match(self._VALID_URL, url).group('id', 'type')
webpage = self._download_webpage(url, main_id)
title = self._html_search_regex(
r'<title\b[^>]*?>(?:Video:\s+)?(.+?)(?:\s+-\s+ThisVid(?:\.com| tube))?</title>',
webpage, 'title')
if type_ == 'embed':
# look for more metadata
video_alt_url = url_or_none(self._search_regex(
r'''video_alt_url\s*:\s+'(%s/)',''' % (self._VALID_URL, ),
webpage, 'video_alt_url', default=None))
if video_alt_url and video_alt_url != url:
webpage = self._download_webpage(
video_alt_url, main_id,
note='Redirecting embed to main page', fatal=False) or webpage
video_holder = get_element_by_class('video-holder', webpage) or ''
if '>This video is a private video' in video_holder:
self.raise_login_required(
(clean_html(video_holder) or 'Private video').split('\n', 1)[0])
uploader = self._html_search_regex(
r'''(?s)<span\b[^>]*>Added by:\s*</span><a\b[^>]+\bclass\s*=\s*["']author\b[^>]+\bhref\s*=\s*["']https://thisvid\.com/members/([0-9]+/.{3,}?)\s*</a>''',
webpage, 'uploader', default='')
uploader = re.split(r'''/["'][^>]*>\s*''', uploader)
if len(uploader) == 2:
# id must be non-empty, uploader could be ''
uploader_id, uploader = uploader
uploader = uploader or None
else:
uploader_id = uploader = None
return merge_dicts({
'_type': 'url_transparent',
'title': title,
'age_limit': 18,
'uploader': uploader,
'uploader_id': uploader_id,
}, self.url_result(url, ie='Generic'))
class ThisVidMemberIE(InfoExtractor):
_VALID_URL = r'https?://thisvid\.com/members/(?P<id>\d+)'
_TESTS = [{
'url': 'https://thisvid.com/members/2140501/',
'info_dict': {
'id': '2140501',
'title': 'Rafflesia\'s Profile',
},
'playlist_mincount': 16,
}, {
'url': 'https://thisvid.com/members/2140501/favourite_videos/',
'info_dict': {
'id': '2140501',
'title': 'Rafflesia\'s Favourite Videos',
},
'playlist_mincount': 15,
}, {
'url': 'https://thisvid.com/members/636468/public_videos/',
'info_dict': {
'id': '636468',
'title': 'Happymouth\'s Public Videos',
},
'playlist_mincount': 196,
},
]
def _urls(self, html):
for m in re.finditer(r'''<a\b[^>]+\bhref\s*=\s*["'](?P<url>%s\b)[^>]+>''' % (ThisVidIE._VALID_URL, ), html):
yield m.group('url')
def _real_extract(self, url):
pl_id = self._match_id(url)
webpage = self._download_webpage(url, pl_id)
title = re.split(
r'(?i)\s*\|\s*ThisVid\.com\s*$',
self._og_search_title(webpage, default=None) or self._html_search_regex(r'(?s)<title\b[^>]*>(.+?)</title', webpage, 'title', fatal=False) or '', 1)[0] or None
def entries(page_url, html=None):
for page in itertools.count(1):
if not html:
html = self._download_webpage(
page_url, pl_id, note='Downloading page %d' % (page, ),
fatal=False) or ''
for u in self._urls(html):
yield u
next_page = get_element_by_class('pagination-next', html) or ''
if next_page:
# member list page
next_page = urljoin(url, self._search_regex(
r'''<a\b[^>]+\bhref\s*=\s*("|')(?P<url>(?!#)(?:(?!\1).)+)''',
next_page, 'next page link', group='url', default=None))
# in case a member page should have pagination-next with empty link, not just `else:`
if next_page is None:
# playlist page
parsed_url = compat_urlparse.urlparse(page_url)
base_path, num = parsed_url.path.rsplit('/', 1)
num = int_or_none(num)
if num is None:
base_path, num = parsed_url.path.rstrip('/'), 1
parsed_url = parsed_url._replace(path=base_path + ('/%d' % (num + 1, )))
next_page = compat_urlparse.urlunparse(parsed_url)
if page_url == next_page:
next_page = None
if not next_page:
break
page_url, html = next_page, None
return self.playlist_from_matches(
entries(url, webpage), playlist_id=pl_id, playlist_title=title, ie='ThisVid')
class ThisVidPlaylistIE(ThisVidMemberIE):
_VALID_URL = r'https?://thisvid\.com/playlist/(?P<id>\d+)/video/(?P<video_id>[A-Za-z0-9-]+)'
_TESTS = [{
'url': 'https://thisvid.com/playlist/6615/video/big-italian-booty-28/',
'info_dict': {
'id': '6615',
'title': 'Underwear Stuff',
},
'playlist_mincount': 200,
}, {
'url': 'https://thisvid.com/playlist/6615/video/big-italian-booty-28/',
'info_dict': {
'id': '1072387',
'ext': 'mp4',
'title': 'Big Italian Booty 28',
'description': 'md5:1bccf7b13765e18fb27bf764dba7ede2',
'uploader_id': '367912',
'uploader': 'Jcmusclefun',
'age_limit': 18,
},
'params': {
'noplaylist': True,
},
}]
def _get_video_url(self, pl_url):
video_id = re.match(self._VALID_URL, pl_url).group('video_id')
return urljoin(pl_url, '/videos/%s/' % (video_id, ))
def _urls(self, html):
for m in re.finditer(r'''<a\b[^>]+\bhref\s*=\s*["'](?P<url>%s\b)[^>]+>''' % (self._VALID_URL, ), html):
yield self._get_video_url(m.group('url'))
def _real_extract(self, url):
pl_id = self._match_id(url)
if self._downloader.params.get('noplaylist'):
self.to_screen('Downloading just the featured video because of --no-playlist')
return self.url_result(self._get_video_url(url), 'ThisVid')
self.to_screen(
'Downloading playlist %s - add --no-playlist to download just the featured video' % (pl_id, ))
result = super(ThisVidPlaylistIE, self)._real_extract(url)
# rework title returned as `the title - the title`
title = result['title']
t_len = len(title)
if t_len > 5 and t_len % 2 != 0:
t_len = t_len // 2
if title[t_len] == '-':
title = [t.strip() for t in (title[:t_len], title[t_len + 1:])]
if title[0] and title[0] == title[1]:
result['title'] = title[0]
return result
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/metacafe.py | youtube_dl/extractor/metacafe.py | from __future__ import unicode_literals
import json
import re
from .common import InfoExtractor
from ..compat import (
compat_parse_qs,
compat_urllib_parse,
compat_urllib_parse_unquote,
)
from ..utils import (
determine_ext,
ExtractorError,
int_or_none,
get_element_by_attribute,
mimetype2ext,
)
class MetacafeIE(InfoExtractor):
_VALID_URL = r'https?://(?:www\.)?metacafe\.com/watch/(?P<video_id>[^/]+)/(?P<display_id>[^/?#]+)'
_DISCLAIMER = 'http://www.metacafe.com/family_filter/'
_FILTER_POST = 'http://www.metacafe.com/f/index.php?inputType=filter&controllerGroup=user'
IE_NAME = 'metacafe'
_TESTS = [
# Youtube video
{
'add_ie': ['Youtube'],
'url': 'http://metacafe.com/watch/yt-_aUehQsCQtM/the_electric_company_short_i_pbs_kids_go/',
'info_dict': {
'id': '_aUehQsCQtM',
'ext': 'mp4',
'upload_date': '20090102',
'title': 'The Electric Company | "Short I" | PBS KIDS GO!',
'description': 'md5:2439a8ef6d5a70e380c22f5ad323e5a8',
'uploader': 'PBS',
'uploader_id': 'PBS'
}
},
# Normal metacafe video
{
'url': 'http://www.metacafe.com/watch/11121940/news_stuff_you_wont_do_with_your_playstation_4/',
'md5': '6e0bca200eaad2552e6915ed6fd4d9ad',
'info_dict': {
'id': '11121940',
'ext': 'mp4',
'title': 'News: Stuff You Won\'t Do with Your PlayStation 4',
'uploader': 'ign',
'description': 'Sony released a massive FAQ on the PlayStation Blog detailing the PS4\'s capabilities and limitations.',
},
'skip': 'Page is temporarily unavailable.',
},
# metacafe video with family filter
{
'url': 'http://www.metacafe.com/watch/2155630/adult_art_by_david_hart_156/',
'md5': 'b06082c5079bbdcde677a6291fbdf376',
'info_dict': {
'id': '2155630',
'ext': 'mp4',
'title': 'Adult Art By David Hart 156',
'uploader': '63346',
'description': 'md5:9afac8fc885252201ad14563694040fc',
},
'params': {
'skip_download': True,
},
},
# AnyClip video
{
'url': 'http://www.metacafe.com/watch/an-dVVXnuY7Jh77J/the_andromeda_strain_1971_stop_the_bomb_part_3/',
'info_dict': {
'id': 'an-dVVXnuY7Jh77J',
'ext': 'mp4',
'title': 'The Andromeda Strain (1971): Stop the Bomb Part 3',
'uploader': 'AnyClip',
'description': 'md5:cbef0460d31e3807f6feb4e7a5952e5b',
},
},
# age-restricted video
{
'url': 'http://www.metacafe.com/watch/5186653/bbc_internal_christmas_tape_79_uncensored_outtakes_etc/',
'md5': '98dde7c1a35d02178e8ab7560fe8bd09',
'info_dict': {
'id': '5186653',
'ext': 'mp4',
'title': 'BBC INTERNAL Christmas Tape \'79 - UNCENSORED Outtakes, Etc.',
'uploader': 'Dwayne Pipe',
'description': 'md5:950bf4c581e2c059911fa3ffbe377e4b',
'age_limit': 18,
},
},
# cbs video
{
'url': 'http://www.metacafe.com/watch/cb-8VD4r_Zws8VP/open_this_is_face_the_nation_february_9/',
'info_dict': {
'id': '8VD4r_Zws8VP',
'ext': 'flv',
'title': 'Open: This is Face the Nation, February 9',
'description': 'md5:8a9ceec26d1f7ed6eab610834cc1a476',
'duration': 96,
'uploader': 'CBSI-NEW',
'upload_date': '20140209',
'timestamp': 1391959800,
},
'params': {
# rtmp download
'skip_download': True,
},
},
# Movieclips.com video
{
'url': 'http://www.metacafe.com/watch/mv-Wy7ZU/my_week_with_marilyn_do_you_love_me/',
'info_dict': {
'id': 'mv-Wy7ZU',
'ext': 'mp4',
'title': 'My Week with Marilyn - Do You Love Me?',
'description': 'From the movie My Week with Marilyn - Colin (Eddie Redmayne) professes his love to Marilyn (Michelle Williams) and gets her to promise to return to set and finish the movie.',
'uploader': 'movie_trailers',
'duration': 176,
},
'params': {
'skip_download': 'requires rtmpdump',
}
}
]
def report_disclaimer(self):
self.to_screen('Retrieving disclaimer')
def _real_extract(self, url):
# Extract id and simplified title from URL
video_id, display_id = re.match(self._VALID_URL, url).groups()
# the video may come from an external site
m_external = re.match(r'^(\w{2})-(.*)$', video_id)
if m_external is not None:
prefix, ext_id = m_external.groups()
# Check if video comes from YouTube
if prefix == 'yt':
return self.url_result('http://www.youtube.com/watch?v=%s' % ext_id, 'Youtube')
# CBS videos use theplatform.com
if prefix == 'cb':
return self.url_result('theplatform:%s' % ext_id, 'ThePlatform')
headers = {
# Disable family filter
'Cookie': 'user=%s; ' % compat_urllib_parse.quote(json.dumps({'ffilter': False}))
}
# AnyClip videos require the flashversion cookie so that we get the link
# to the mp4 file
if video_id.startswith('an-'):
headers['Cookie'] += 'flashVersion=0; '
# Retrieve video webpage to extract further information
webpage = self._download_webpage(url, video_id, headers=headers)
error = get_element_by_attribute(
'class', 'notfound-page-title', webpage)
if error:
raise ExtractorError(error, expected=True)
video_title = self._html_search_meta(
['og:title', 'twitter:title'], webpage, 'title', default=None) or self._search_regex(r'<h1>(.*?)</h1>', webpage, 'title')
# Extract URL, uploader and title from webpage
self.report_extraction(video_id)
video_url = None
mobj = re.search(r'(?m)&(?:media|video)URL=([^&]+)', webpage)
if mobj is not None:
mediaURL = compat_urllib_parse_unquote(mobj.group(1))
video_ext = determine_ext(mediaURL)
# Extract gdaKey if available
mobj = re.search(r'(?m)&gdaKey=(.*?)&', webpage)
if mobj is None:
video_url = mediaURL
else:
gdaKey = mobj.group(1)
video_url = '%s?__gda__=%s' % (mediaURL, gdaKey)
if video_url is None:
mobj = re.search(r'<video src="([^"]+)"', webpage)
if mobj:
video_url = mobj.group(1)
video_ext = 'mp4'
if video_url is None:
flashvars = self._search_regex(
r' name="flashvars" value="(.*?)"', webpage, 'flashvars',
default=None)
if flashvars:
vardict = compat_parse_qs(flashvars)
if 'mediaData' not in vardict:
raise ExtractorError('Unable to extract media URL')
mobj = re.search(
r'"mediaURL":"(?P<mediaURL>http.*?)",(.*?)"key":"(?P<key>.*?)"', vardict['mediaData'][0])
if mobj is None:
raise ExtractorError('Unable to extract media URL')
mediaURL = mobj.group('mediaURL').replace('\\/', '/')
video_url = '%s?__gda__=%s' % (mediaURL, mobj.group('key'))
video_ext = determine_ext(video_url)
if video_url is None:
player_url = self._search_regex(
r"swfobject\.embedSWF\('([^']+)'",
webpage, 'config URL', default=None)
if player_url:
config_url = self._search_regex(
r'config=(.+)$', player_url, 'config URL')
config_doc = self._download_xml(
config_url, video_id,
note='Downloading video config')
smil_url = config_doc.find('.//properties').attrib['smil_file']
smil_doc = self._download_xml(
smil_url, video_id,
note='Downloading SMIL document')
base_url = smil_doc.find('./head/meta').attrib['base']
video_url = []
for vn in smil_doc.findall('.//video'):
br = int(vn.attrib['system-bitrate'])
play_path = vn.attrib['src']
video_url.append({
'format_id': 'smil-%d' % br,
'url': base_url,
'play_path': play_path,
'page_url': url,
'player_url': player_url,
'ext': play_path.partition(':')[0],
})
if video_url is None:
flashvars = self._parse_json(self._search_regex(
r'flashvars\s*=\s*({.*});', webpage, 'flashvars',
default=None), video_id, fatal=False)
if flashvars:
video_url = []
for source in flashvars.get('sources'):
source_url = source.get('src')
if not source_url:
continue
ext = mimetype2ext(source.get('type')) or determine_ext(source_url)
if ext == 'm3u8':
video_url.extend(self._extract_m3u8_formats(
source_url, video_id, 'mp4',
'm3u8_native', m3u8_id='hls', fatal=False))
else:
video_url.append({
'url': source_url,
'ext': ext,
})
if video_url is None:
raise ExtractorError('Unsupported video type')
description = self._html_search_meta(
['og:description', 'twitter:description', 'description'],
webpage, 'title', fatal=False)
thumbnail = self._html_search_meta(
['og:image', 'twitter:image'], webpage, 'title', fatal=False)
video_uploader = self._html_search_regex(
r'submitter=(.*?);|googletag\.pubads\(\)\.setTargeting\("(?:channel|submiter)","([^"]+)"\);',
webpage, 'uploader nickname', fatal=False)
duration = int_or_none(
self._html_search_meta('video:duration', webpage, default=None))
age_limit = (
18
if re.search(r'(?:"contentRating":|"rating",)"restricted"', webpage)
else 0)
if isinstance(video_url, list):
formats = video_url
else:
formats = [{
'url': video_url,
'ext': video_ext,
}]
self._sort_formats(formats)
return {
'id': video_id,
'display_id': display_id,
'description': description,
'uploader': video_uploader,
'title': video_title,
'thumbnail': thumbnail,
'age_limit': age_limit,
'formats': formats,
'duration': duration,
}
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/ivideon.py | youtube_dl/extractor/ivideon.py | # coding: utf-8
from __future__ import unicode_literals
import re
from .common import InfoExtractor
from ..compat import (
compat_urllib_parse_urlencode,
compat_urlparse,
)
from ..utils import qualities
class IvideonIE(InfoExtractor):
IE_NAME = 'ivideon'
IE_DESC = 'Ivideon TV'
_VALID_URL = r'https?://(?:www\.)?ivideon\.com/tv/(?:[^/]+/)*camera/(?P<id>\d+-[\da-f]+)/(?P<camera_id>\d+)'
_TESTS = [{
'url': 'https://www.ivideon.com/tv/camera/100-916ca13b5c4ad9f564266424a026386d/0/',
'info_dict': {
'id': '100-916ca13b5c4ad9f564266424a026386d',
'ext': 'flv',
'title': 're:^Касса [0-9]{4}-[0-9]{2}-[0-9]{2} [0-9]{2}:[0-9]{2}$',
'description': 'Основное предназначение - запись действий кассиров. Плюс общий вид.',
'is_live': True,
},
'params': {
'skip_download': True,
}
}, {
'url': 'https://www.ivideon.com/tv/camera/100-c4ee4cb9ede885cf62dfbe93d7b53783/589824/?lang=ru',
'only_matching': True,
}, {
'url': 'https://www.ivideon.com/tv/map/22.917923/-31.816406/16/camera/100-e7bc16c7d4b5bbd633fd5350b66dfa9a/0',
'only_matching': True,
}]
_QUALITIES = ('low', 'mid', 'hi')
def _real_extract(self, url):
mobj = re.match(self._VALID_URL, url)
server_id, camera_id = mobj.group('id'), mobj.group('camera_id')
camera_name, description = None, None
camera_url = compat_urlparse.urljoin(
url, '/tv/camera/%s/%s/' % (server_id, camera_id))
webpage = self._download_webpage(camera_url, server_id, fatal=False)
if webpage:
config_string = self._search_regex(
r'var\s+config\s*=\s*({.+?});', webpage, 'config', default=None)
if config_string:
config = self._parse_json(config_string, server_id, fatal=False)
camera_info = config.get('ivTvAppOptions', {}).get('currentCameraInfo')
if camera_info:
camera_name = camera_info.get('camera_name')
description = camera_info.get('misc', {}).get('description')
if not camera_name:
camera_name = self._html_search_meta(
'name', webpage, 'camera name', default=None) or self._search_regex(
r'<h1[^>]+class="b-video-title"[^>]*>([^<]+)', webpage, 'camera name', default=None)
quality = qualities(self._QUALITIES)
formats = [{
'url': 'https://streaming.ivideon.com/flv/live?%s' % compat_urllib_parse_urlencode({
'server': server_id,
'camera': camera_id,
'sessionId': 'demo',
'q': quality(format_id),
}),
'format_id': format_id,
'ext': 'flv',
'quality': quality(format_id),
} for format_id in self._QUALITIES]
self._sort_formats(formats)
return {
'id': server_id,
'title': self._live_title(camera_name or server_id),
'description': description,
'is_live': True,
'formats': formats,
}
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/litv.py | youtube_dl/extractor/litv.py | # coding: utf-8
from __future__ import unicode_literals
import json
from .common import InfoExtractor
from ..utils import (
ExtractorError,
int_or_none,
smuggle_url,
unsmuggle_url,
)
class LiTVIE(InfoExtractor):
_VALID_URL = r'https?://(?:www\.)?litv\.tv/(?:vod|promo)/[^/]+/(?:content\.do)?\?.*?\b(?:content_)?id=(?P<id>[^&]+)'
_URL_TEMPLATE = 'https://www.litv.tv/vod/%s/content.do?id=%s'
_TESTS = [{
'url': 'https://www.litv.tv/vod/drama/content.do?brc_id=root&id=VOD00041610&isUHEnabled=true&autoPlay=1',
'info_dict': {
'id': 'VOD00041606',
'title': '花千骨',
},
'playlist_count': 50,
}, {
'url': 'https://www.litv.tv/vod/drama/content.do?brc_id=root&id=VOD00041610&isUHEnabled=true&autoPlay=1',
'md5': '969e343d9244778cb29acec608e53640',
'info_dict': {
'id': 'VOD00041610',
'ext': 'mp4',
'title': '花千骨第1集',
'thumbnail': r're:https?://.*\.jpg$',
'description': 'md5:c7017aa144c87467c4fb2909c4b05d6f',
'episode_number': 1,
},
'params': {
'noplaylist': True,
},
'skip': 'Georestricted to Taiwan',
}, {
'url': 'https://www.litv.tv/promo/miyuezhuan/?content_id=VOD00044841&',
'md5': '88322ea132f848d6e3e18b32a832b918',
'info_dict': {
'id': 'VOD00044841',
'ext': 'mp4',
'title': '芈月傳第1集 霸星芈月降世楚國',
'description': '楚威王二年，太史令唐昧夜觀星象，發現霸星即將現世。王后得知霸星的預言後，想盡辦法不讓孩子順利出生，幸得莒姬相護化解危機。沒想到眾人期待下出生的霸星卻是位公主，楚威王對此失望至極。楚王后命人將女嬰丟棄河中，居然奇蹟似的被少司命像攔下，楚威王認為此女非同凡響，為她取名芈月。',
},
'skip': 'Georestricted to Taiwan',
}]
def _extract_playlist(self, season_list, video_id, program_info, prompt=True):
episode_title = program_info['title']
content_id = season_list['contentId']
if prompt:
self.to_screen('Downloading playlist %s - add --no-playlist to just download video %s' % (content_id, video_id))
all_episodes = [
self.url_result(smuggle_url(
self._URL_TEMPLATE % (program_info['contentType'], episode['contentId']),
{'force_noplaylist': True})) # To prevent infinite recursion
for episode in season_list['episode']]
return self.playlist_result(all_episodes, content_id, episode_title)
def _real_extract(self, url):
url, data = unsmuggle_url(url, {})
video_id = self._match_id(url)
noplaylist = self._downloader.params.get('noplaylist')
noplaylist_prompt = True
if 'force_noplaylist' in data:
noplaylist = data['force_noplaylist']
noplaylist_prompt = False
webpage = self._download_webpage(url, video_id)
program_info = self._parse_json(self._search_regex(
r'var\s+programInfo\s*=\s*([^;]+)', webpage, 'VOD data', default='{}'),
video_id)
season_list = list(program_info.get('seasonList', {}).values())
if season_list:
if not noplaylist:
return self._extract_playlist(
season_list[0], video_id, program_info,
prompt=noplaylist_prompt)
if noplaylist_prompt:
self.to_screen('Downloading just video %s because of --no-playlist' % video_id)
# In browsers `getMainUrl` request is always issued. Usually this
# endpoint gives the same result as the data embedded in the webpage.
# If georestricted, there are no embedded data, so an extra request is
# necessary to get the error code
if 'assetId' not in program_info:
program_info = self._download_json(
'https://www.litv.tv/vod/ajax/getProgramInfo', video_id,
query={'contentId': video_id},
headers={'Accept': 'application/json'})
video_data = self._parse_json(self._search_regex(
r'uiHlsUrl\s*=\s*testBackendData\(([^;]+)\);',
webpage, 'video data', default='{}'), video_id)
if not video_data:
payload = {
'assetId': program_info['assetId'],
'watchDevices': program_info['watchDevices'],
'contentType': program_info['contentType'],
}
video_data = self._download_json(
'https://www.litv.tv/vod/getMainUrl', video_id,
data=json.dumps(payload).encode('utf-8'),
headers={'Content-Type': 'application/json'})
if not video_data.get('fullpath'):
error_msg = video_data.get('errorMessage')
if error_msg == 'vod.error.outsideregionerror':
self.raise_geo_restricted('This video is available in Taiwan only')
if error_msg:
raise ExtractorError('%s said: %s' % (self.IE_NAME, error_msg), expected=True)
raise ExtractorError('Unexpected result from %s' % self.IE_NAME)
formats = self._extract_m3u8_formats(
video_data['fullpath'], video_id, ext='mp4',
entry_protocol='m3u8_native', m3u8_id='hls')
for a_format in formats:
# LiTV HLS segments doesn't like compressions
a_format.setdefault('http_headers', {})['Youtubedl-no-compression'] = True
title = program_info['title'] + program_info.get('secondaryMark', '')
description = program_info.get('description')
thumbnail = program_info.get('imageFile')
categories = [item['name'] for item in program_info.get('category', [])]
episode = int_or_none(program_info.get('episode'))
return {
'id': video_id,
'formats': formats,
'title': title,
'description': description,
'thumbnail': thumbnail,
'categories': categories,
'episode_number': episode,
}
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/gdcvault.py | youtube_dl/extractor/gdcvault.py | from __future__ import unicode_literals
import re
from .common import InfoExtractor
from .kaltura import KalturaIE
from ..utils import (
HEADRequest,
remove_start,
sanitized_Request,
smuggle_url,
urlencode_postdata,
)
class GDCVaultIE(InfoExtractor):
_VALID_URL = r'https?://(?:www\.)?gdcvault\.com/play/(?P<id>\d+)(?:/(?P<name>[\w-]+))?'
_NETRC_MACHINE = 'gdcvault'
_TESTS = [
{
'url': 'http://www.gdcvault.com/play/1019721/Doki-Doki-Universe-Sweet-Simple',
'md5': '7ce8388f544c88b7ac11c7ab1b593704',
'info_dict': {
'id': '201311826596_AWNY',
'display_id': 'Doki-Doki-Universe-Sweet-Simple',
'ext': 'mp4',
'title': 'Doki-Doki Universe: Sweet, Simple and Genuine (GDC Next 10)'
}
},
{
'url': 'http://www.gdcvault.com/play/1015683/Embracing-the-Dark-Art-of',
'info_dict': {
'id': '201203272_1330951438328RSXR',
'display_id': 'Embracing-the-Dark-Art-of',
'ext': 'flv',
'title': 'Embracing the Dark Art of Mathematical Modeling in AI'
},
'params': {
'skip_download': True, # Requires rtmpdump
}
},
{
'url': 'http://www.gdcvault.com/play/1015301/Thexder-Meets-Windows-95-or',
'md5': 'a5eb77996ef82118afbbe8e48731b98e',
'info_dict': {
'id': '1015301',
'display_id': 'Thexder-Meets-Windows-95-or',
'ext': 'flv',
'title': 'Thexder Meets Windows 95, or Writing Great Games in the Windows 95 Environment',
},
'skip': 'Requires login',
},
{
'url': 'http://gdcvault.com/play/1020791/',
'only_matching': True,
},
{
# Hard-coded hostname
'url': 'http://gdcvault.com/play/1023460/Tenacious-Design-and-The-Interface',
'md5': 'a8efb6c31ed06ca8739294960b2dbabd',
'info_dict': {
'id': '840376_BQRC',
'ext': 'mp4',
'display_id': 'Tenacious-Design-and-The-Interface',
'title': 'Tenacious Design and The Interface of \'Destiny\'',
},
},
{
# Multiple audios
'url': 'http://www.gdcvault.com/play/1014631/Classic-Game-Postmortem-PAC',
'info_dict': {
'id': '12396_1299111843500GMPX',
'ext': 'mp4',
'title': 'How to Create a Good Game - From My Experience of Designing Pac-Man',
},
# 'params': {
# 'skip_download': True, # Requires rtmpdump
# 'format': 'jp', # The japanese audio
# }
},
{
# gdc-player.html
'url': 'http://www.gdcvault.com/play/1435/An-American-engine-in-Tokyo',
'info_dict': {
'id': '9350_1238021887562UHXB',
'display_id': 'An-American-engine-in-Tokyo',
'ext': 'mp4',
'title': 'An American Engine in Tokyo:/nThe collaboration of Epic Games and Square Enix/nFor THE LAST REMINANT',
},
},
{
# Kaltura Embed
'url': 'https://www.gdcvault.com/play/1026180/Mastering-the-Apex-of-Scaling',
'info_dict': {
'id': '0_h1fg8j3p',
'ext': 'mp4',
'title': 'Mastering the Apex of Scaling Game Servers (Presented by Multiplay)',
'timestamp': 1554401811,
'upload_date': '20190404',
'uploader_id': 'joe@blazestreaming.com',
},
'params': {
'format': 'mp4-408',
},
},
{
# Kaltura embed, whitespace between quote and embedded URL in iframe's src
'url': 'https://www.gdcvault.com/play/1025699',
'info_dict': {
'id': '0_zagynv0a',
'ext': 'mp4',
'title': 'Tech Toolbox',
'upload_date': '20190408',
'uploader_id': 'joe@blazestreaming.com',
'timestamp': 1554764629,
},
'params': {
'skip_download': True,
},
},
{
# HTML5 video
'url': 'http://www.gdcvault.com/play/1014846/Conference-Keynote-Shigeru',
'only_matching': True,
},
]
def _login(self, webpage_url, display_id):
username, password = self._get_login_info()
if username is None or password is None:
self.report_warning('It looks like ' + webpage_url + ' requires a login. Try specifying a username and password and try again.')
return None
mobj = re.match(r'(?P<root_url>https?://.*?/).*', webpage_url)
login_url = mobj.group('root_url') + 'api/login.php'
logout_url = mobj.group('root_url') + 'logout'
login_form = {
'email': username,
'password': password,
}
request = sanitized_Request(login_url, urlencode_postdata(login_form))
request.add_header('Content-Type', 'application/x-www-form-urlencoded')
self._download_webpage(request, display_id, 'Logging in')
start_page = self._download_webpage(webpage_url, display_id, 'Getting authenticated video page')
self._download_webpage(logout_url, display_id, 'Logging out')
return start_page
def _real_extract(self, url):
video_id, name = re.match(self._VALID_URL, url).groups()
display_id = name or video_id
webpage_url = 'http://www.gdcvault.com/play/' + video_id
start_page = self._download_webpage(webpage_url, display_id)
direct_url = self._search_regex(
r's1\.addVariable\("file",\s*encodeURIComponent\("(/[^"]+)"\)\);',
start_page, 'url', default=None)
if direct_url:
title = self._html_search_regex(
r'<td><strong>Session Name:?</strong></td>\s*<td>(.*?)</td>',
start_page, 'title')
video_url = 'http://www.gdcvault.com' + direct_url
# resolve the url so that we can detect the correct extension
video_url = self._request_webpage(
HEADRequest(video_url), video_id).geturl()
return {
'id': video_id,
'display_id': display_id,
'url': video_url,
'title': title,
}
embed_url = KalturaIE._extract_url(start_page)
if embed_url:
embed_url = smuggle_url(embed_url, {'source_url': url})
ie_key = 'Kaltura'
else:
PLAYER_REGEX = r'<iframe src="(?P<xml_root>.+?)/(?:gdc-)?player.*?\.html.*?".*?</iframe>'
xml_root = self._html_search_regex(
PLAYER_REGEX, start_page, 'xml root', default=None)
if xml_root is None:
# Probably need to authenticate
login_res = self._login(webpage_url, display_id)
if login_res is None:
self.report_warning('Could not login.')
else:
start_page = login_res
# Grab the url from the authenticated page
xml_root = self._html_search_regex(
PLAYER_REGEX, start_page, 'xml root')
xml_name = self._html_search_regex(
r'<iframe src=".*?\?xml(?:=|URL=xml/)(.+?\.xml).*?".*?</iframe>',
start_page, 'xml filename', default=None)
if not xml_name:
info = self._parse_html5_media_entries(url, start_page, video_id)[0]
info.update({
'title': remove_start(self._search_regex(
r'>Session Name:\s*<.*?>\s*<td>(.+?)</td>', start_page,
'title', default=None) or self._og_search_title(
start_page, default=None), 'GDC Vault - '),
'id': video_id,
'display_id': display_id,
})
return info
embed_url = '%s/xml/%s' % (xml_root, xml_name)
ie_key = 'DigitallySpeaking'
return {
'_type': 'url_transparent',
'id': video_id,
'display_id': display_id,
'url': embed_url,
'ie_key': ie_key,
}
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/stretchinternet.py | youtube_dl/extractor/stretchinternet.py | from __future__ import unicode_literals
from .common import InfoExtractor
class StretchInternetIE(InfoExtractor):
_VALID_URL = r'https?://portal\.stretchinternet\.com/[^/]+/(?:portal|full)\.htm\?.*?\beventId=(?P<id>\d+)'
_TEST = {
'url': 'https://portal.stretchinternet.com/umary/portal.htm?eventId=573272&streamType=video',
'info_dict': {
'id': '573272',
'ext': 'mp4',
'title': 'UNIVERSITY OF MARY WRESTLING VS UPPER IOWA',
# 'timestamp': 1575668361,
# 'upload_date': '20191206',
'uploader_id': '99997',
}
}
def _real_extract(self, url):
video_id = self._match_id(url)
media_url = self._download_json(
'https://core.stretchlive.com/trinity/event/tcg/' + video_id,
video_id)[0]['media'][0]['url']
event = self._download_json(
'https://neo-client.stretchinternet.com/portal-ws/getEvent.json',
video_id, query={'eventID': video_id, 'token': 'asdf'})['event']
return {
'id': video_id,
'title': event['title'],
# TODO: parse US timezone abbreviations
# 'timestamp': event.get('dateTimeString'),
'url': 'https://' + media_url,
'uploader_id': event.get('ownerID'),
}
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/theplatform.py | youtube_dl/extractor/theplatform.py | # coding: utf-8
from __future__ import unicode_literals
import re
import time
import hmac
import binascii
import hashlib
from .once import OnceIE
from .adobepass import AdobePassIE
from ..compat import (
compat_parse_qs,
compat_urllib_parse_urlparse,
)
from ..utils import (
determine_ext,
ExtractorError,
float_or_none,
int_or_none,
sanitized_Request,
unsmuggle_url,
update_url_query,
xpath_with_ns,
mimetype2ext,
find_xpath_attr,
)
default_ns = 'http://www.w3.org/2005/SMIL21/Language'
_x = lambda p: xpath_with_ns(p, {'smil': default_ns})
class ThePlatformBaseIE(OnceIE):
_TP_TLD = 'com'
def _extract_theplatform_smil(self, smil_url, video_id, note='Downloading SMIL data'):
meta = self._download_xml(
smil_url, video_id, note=note, query={'format': 'SMIL'},
headers=self.geo_verification_headers())
error_element = find_xpath_attr(meta, _x('.//smil:ref'), 'src')
if error_element is not None:
exception = find_xpath_attr(
error_element, _x('.//smil:param'), 'name', 'exception')
if exception is not None:
if exception.get('value') == 'GeoLocationBlocked':
self.raise_geo_restricted(error_element.attrib['abstract'])
elif error_element.attrib['src'].startswith(
'http://link.theplatform.%s/s/errorFiles/Unavailable.'
% self._TP_TLD):
raise ExtractorError(
error_element.attrib['abstract'], expected=True)
smil_formats = self._parse_smil_formats(
meta, smil_url, video_id, namespace=default_ns,
# the parameters are from syfy.com, other sites may use others,
# they also work for nbc.com
f4m_params={'g': 'UXWGVKRWHFSP', 'hdcore': '3.0.3'},
transform_rtmp_url=lambda streamer, src: (streamer, 'mp4:' + src))
formats = []
for _format in smil_formats:
if OnceIE.suitable(_format['url']):
formats.extend(self._extract_once_formats(_format['url']))
else:
media_url = _format['url']
if determine_ext(media_url) == 'm3u8':
hdnea2 = self._get_cookies(media_url).get('hdnea2')
if hdnea2:
_format['url'] = update_url_query(media_url, {'hdnea3': hdnea2.value})
formats.append(_format)
subtitles = self._parse_smil_subtitles(meta, default_ns)
return formats, subtitles
def _download_theplatform_metadata(self, path, video_id):
info_url = 'http://link.theplatform.%s/s/%s?format=preview' % (self._TP_TLD, path)
return self._download_json(info_url, video_id)
def _parse_theplatform_metadata(self, info):
subtitles = {}
captions = info.get('captions')
if isinstance(captions, list):
for caption in captions:
lang, src, mime = caption.get('lang', 'en'), caption.get('src'), caption.get('type')
subtitles.setdefault(lang, []).append({
'ext': mimetype2ext(mime),
'url': src,
})
duration = info.get('duration')
tp_chapters = info.get('chapters', [])
chapters = []
if tp_chapters:
def _add_chapter(start_time, end_time):
start_time = float_or_none(start_time, 1000)
end_time = float_or_none(end_time, 1000)
if start_time is None or end_time is None:
return
chapters.append({
'start_time': start_time,
'end_time': end_time,
})
for chapter in tp_chapters[:-1]:
_add_chapter(chapter.get('startTime'), chapter.get('endTime'))
_add_chapter(tp_chapters[-1].get('startTime'), tp_chapters[-1].get('endTime') or duration)
return {
'title': info['title'],
'subtitles': subtitles,
'description': info['description'],
'thumbnail': info['defaultThumbnailUrl'],
'duration': float_or_none(duration, 1000),
'timestamp': int_or_none(info.get('pubDate'), 1000) or None,
'uploader': info.get('billingCode'),
'chapters': chapters,
}
def _extract_theplatform_metadata(self, path, video_id):
info = self._download_theplatform_metadata(path, video_id)
return self._parse_theplatform_metadata(info)
class ThePlatformIE(ThePlatformBaseIE, AdobePassIE):
_VALID_URL = r'''(?x)
(?:https?://(?:link|player)\.theplatform\.com/[sp]/(?P<provider_id>[^/]+)/
(?:(?:(?:[^/]+/)+select/)?(?P<media>media/(?:guid/\d+/)?)?|(?P<config>(?:[^/\?]+/(?:swf|config)|onsite)/select/))?
|theplatform:)(?P<id>[^/\?&]+)'''
_TESTS = [{
# from http://www.metacafe.com/watch/cb-e9I_cZgTgIPd/blackberrys_big_bold_z30/
'url': 'http://link.theplatform.com/s/dJ5BDC/e9I_cZgTgIPd/meta.smil?format=smil&Tracking=true&mbr=true',
'info_dict': {
'id': 'e9I_cZgTgIPd',
'ext': 'flv',
'title': 'Blackberry\'s big, bold Z30',
'description': 'The Z30 is Blackberry\'s biggest, baddest mobile messaging device yet.',
'duration': 247,
'timestamp': 1383239700,
'upload_date': '20131031',
'uploader': 'CBSI-NEW',
},
'params': {
# rtmp download
'skip_download': True,
},
'skip': '404 Not Found',
}, {
# from http://www.cnet.com/videos/tesla-model-s-a-second-step-towards-a-cleaner-motoring-future/
'url': 'http://link.theplatform.com/s/kYEXFC/22d_qsQ6MIRT',
'info_dict': {
'id': '22d_qsQ6MIRT',
'ext': 'flv',
'description': 'md5:ac330c9258c04f9d7512cf26b9595409',
'title': 'Tesla Model S: A second step towards a cleaner motoring future',
'timestamp': 1426176191,
'upload_date': '20150312',
'uploader': 'CBSI-NEW',
},
'params': {
# rtmp download
'skip_download': True,
}
}, {
'url': 'https://player.theplatform.com/p/D6x-PC/pulse_preview/embed/select/media/yMBg9E8KFxZD',
'info_dict': {
'id': 'yMBg9E8KFxZD',
'ext': 'mp4',
'description': 'md5:644ad9188d655b742f942bf2e06b002d',
'title': 'HIGHLIGHTS: USA bag first ever series Cup win',
'uploader': 'EGSM',
}
}, {
'url': 'http://player.theplatform.com/p/NnzsPC/widget/select/media/4Y0TlYUr_ZT7',
'only_matching': True,
}, {
'url': 'http://player.theplatform.com/p/2E2eJC/nbcNewsOffsite?guid=tdy_or_siri_150701',
'md5': 'fb96bb3d85118930a5b055783a3bd992',
'info_dict': {
'id': 'tdy_or_siri_150701',
'ext': 'mp4',
'title': 'iPhone Siri’s sassy response to a math question has people talking',
'description': 'md5:a565d1deadd5086f3331d57298ec6333',
'duration': 83.0,
'thumbnail': r're:^https?://.*\.jpg$',
'timestamp': 1435752600,
'upload_date': '20150701',
'uploader': 'NBCU-NEWS',
},
}, {
# From http://www.nbc.com/the-blacklist/video/sir-crispin-crandall/2928790?onid=137781#vc137781=1
# geo-restricted (US), HLS encrypted with AES-128
'url': 'http://player.theplatform.com/p/NnzsPC/onsite_universal/select/media/guid/2410887629/2928790?fwsitesection=nbc_the_blacklist_video_library&autoPlay=true&carouselID=137781',
'only_matching': True,
}]
@classmethod
def _extract_urls(cls, webpage):
m = re.search(
r'''(?x)
<meta\s+
property=(["'])(?:og:video(?::(?:secure_)?url)?|twitter:player)\1\s+
content=(["'])(?P<url>https?://player\.theplatform\.com/p/.+?)\2
''', webpage)
if m:
return [m.group('url')]
# Are whitespaces ignored in URLs?
# https://github.com/ytdl-org/youtube-dl/issues/12044
matches = re.findall(
r'(?s)<(?:iframe|script)[^>]+src=(["\'])((?:https?:)?//player\.theplatform\.com/p/.+?)\1', webpage)
if matches:
return [re.sub(r'\s', '', list(zip(*matches))[1][0])]
@staticmethod
def _sign_url(url, sig_key, sig_secret, life=600, include_qs=False):
flags = '10' if include_qs else '00'
expiration_date = '%x' % (int(time.time()) + life)
def str_to_hex(str):
return binascii.b2a_hex(str.encode('ascii')).decode('ascii')
def hex_to_bytes(hex):
return binascii.a2b_hex(hex.encode('ascii'))
relative_path = re.match(r'https?://link\.theplatform\.com/s/([^?]+)', url).group(1)
clear_text = hex_to_bytes(flags + expiration_date + str_to_hex(relative_path))
checksum = hmac.new(sig_key.encode('ascii'), clear_text, hashlib.sha1).hexdigest()
sig = flags + expiration_date + checksum + str_to_hex(sig_secret)
return '%s&sig=%s' % (url, sig)
def _real_extract(self, url):
url, smuggled_data = unsmuggle_url(url, {})
self._initialize_geo_bypass({
'countries': smuggled_data.get('geo_countries'),
})
mobj = re.match(self._VALID_URL, url)
provider_id = mobj.group('provider_id')
video_id = mobj.group('id')
if not provider_id:
provider_id = 'dJ5BDC'
path = provider_id + '/'
if mobj.group('media'):
path += mobj.group('media')
path += video_id
qs_dict = compat_parse_qs(compat_urllib_parse_urlparse(url).query)
if 'guid' in qs_dict:
webpage = self._download_webpage(url, video_id)
scripts = re.findall(r'<script[^>]+src="([^"]+)"', webpage)
feed_id = None
# feed id usually locates in the last script.
# Seems there's no pattern for the interested script filename, so
# I try one by one
for script in reversed(scripts):
feed_script = self._download_webpage(
self._proto_relative_url(script, 'http:'),
video_id, 'Downloading feed script')
feed_id = self._search_regex(
r'defaultFeedId\s*:\s*"([^"]+)"', feed_script,
'default feed id', default=None)
if feed_id is not None:
break
if feed_id is None:
raise ExtractorError('Unable to find feed id')
return self.url_result('http://feed.theplatform.com/f/%s/%s?byGuid=%s' % (
provider_id, feed_id, qs_dict['guid'][0]))
if smuggled_data.get('force_smil_url', False):
smil_url = url
# Explicitly specified SMIL (see https://github.com/ytdl-org/youtube-dl/issues/7385)
elif '/guid/' in url:
headers = {}
source_url = smuggled_data.get('source_url')
if source_url:
headers['Referer'] = source_url
request = sanitized_Request(url, headers=headers)
webpage = self._download_webpage(request, video_id)
smil_url = self._search_regex(
r'<link[^>]+href=(["\'])(?P<url>.+?)\1[^>]+type=["\']application/smil\+xml',
webpage, 'smil url', group='url')
path = self._search_regex(
r'link\.theplatform\.com/s/((?:[^/?#&]+/)+[^/?#&]+)', smil_url, 'path')
smil_url += '?' if '?' not in smil_url else '&' + 'formats=m3u,mpeg4'
elif mobj.group('config'):
config_url = url + '&form=json'
config_url = config_url.replace('swf/', 'config/')
config_url = config_url.replace('onsite/', 'onsite/config/')
config = self._download_json(config_url, video_id, 'Downloading config')
if 'releaseUrl' in config:
release_url = config['releaseUrl']
else:
release_url = 'http://link.theplatform.com/s/%s?mbr=true' % path
smil_url = release_url + '&formats=MPEG4&manifest=f4m'
else:
smil_url = 'http://link.theplatform.com/s/%s?mbr=true' % path
sig = smuggled_data.get('sig')
if sig:
smil_url = self._sign_url(smil_url, sig['key'], sig['secret'])
formats, subtitles = self._extract_theplatform_smil(smil_url, video_id)
self._sort_formats(formats)
ret = self._extract_theplatform_metadata(path, video_id)
combined_subtitles = self._merge_subtitles(ret.get('subtitles', {}), subtitles)
ret.update({
'id': video_id,
'formats': formats,
'subtitles': combined_subtitles,
})
return ret
class ThePlatformFeedIE(ThePlatformBaseIE):
_URL_TEMPLATE = '%s//feed.theplatform.com/f/%s/%s?form=json&%s'
_VALID_URL = r'https?://feed\.theplatform\.com/f/(?P<provider_id>[^/]+)/(?P<feed_id>[^?/]+)\?(?:[^&]+&)*(?P<filter>by(?:Gui|I)d=(?P<id>[^&]+))'
_TESTS = [{
# From http://player.theplatform.com/p/7wvmTC/MSNBCEmbeddedOffSite?guid=n_hardball_5biden_140207
'url': 'http://feed.theplatform.com/f/7wvmTC/msnbc_video-p-test?form=json&pretty=true&range=-40&byGuid=n_hardball_5biden_140207',
'md5': '6e32495b5073ab414471b615c5ded394',
'info_dict': {
'id': 'n_hardball_5biden_140207',
'ext': 'mp4',
'title': 'The Biden factor: will Joe run in 2016?',
'description': 'Could Vice President Joe Biden be preparing a 2016 campaign? Mark Halperin and Sam Stein weigh in.',
'thumbnail': r're:^https?://.*\.jpg$',
'upload_date': '20140208',
'timestamp': 1391824260,
'duration': 467.0,
'categories': ['MSNBC/Issues/Democrats', 'MSNBC/Issues/Elections/Election 2016'],
'uploader': 'NBCU-NEWS',
},
}, {
'url': 'http://feed.theplatform.com/f/2E2eJC/nnd_NBCNews?byGuid=nn_netcast_180306.Copy.01',
'only_matching': True,
}]
def _extract_feed_info(self, provider_id, feed_id, filter_query, video_id, custom_fields=None, asset_types_query={}, account_id=None):
real_url = self._URL_TEMPLATE % (self.http_scheme(), provider_id, feed_id, filter_query)
entry = self._download_json(real_url, video_id)['entries'][0]
main_smil_url = 'http://link.theplatform.com/s/%s/media/guid/%d/%s' % (provider_id, account_id, entry['guid']) if account_id else entry.get('plmedia$publicUrl')
formats = []
subtitles = {}
first_video_id = None
duration = None
asset_types = []
for item in entry['media$content']:
smil_url = item['plfile$url']
cur_video_id = ThePlatformIE._match_id(smil_url)
if first_video_id is None:
first_video_id = cur_video_id
duration = float_or_none(item.get('plfile$duration'))
file_asset_types = item.get('plfile$assetTypes') or compat_parse_qs(compat_urllib_parse_urlparse(smil_url).query)['assetTypes']
for asset_type in file_asset_types:
if asset_type in asset_types:
continue
asset_types.append(asset_type)
query = {
'mbr': 'true',
'formats': item['plfile$format'],
'assetTypes': asset_type,
}
if asset_type in asset_types_query:
query.update(asset_types_query[asset_type])
cur_formats, cur_subtitles = self._extract_theplatform_smil(update_url_query(
main_smil_url or smil_url, query), video_id, 'Downloading SMIL data for %s' % asset_type)
formats.extend(cur_formats)
subtitles = self._merge_subtitles(subtitles, cur_subtitles)
self._sort_formats(formats)
thumbnails = [{
'url': thumbnail['plfile$url'],
'width': int_or_none(thumbnail.get('plfile$width')),
'height': int_or_none(thumbnail.get('plfile$height')),
} for thumbnail in entry.get('media$thumbnails', [])]
timestamp = int_or_none(entry.get('media$availableDate'), scale=1000)
categories = [item['media$name'] for item in entry.get('media$categories', [])]
ret = self._extract_theplatform_metadata('%s/%s' % (provider_id, first_video_id), video_id)
subtitles = self._merge_subtitles(subtitles, ret['subtitles'])
ret.update({
'id': video_id,
'formats': formats,
'subtitles': subtitles,
'thumbnails': thumbnails,
'duration': duration,
'timestamp': timestamp,
'categories': categories,
})
if custom_fields:
ret.update(custom_fields(entry))
return ret
def _real_extract(self, url):
mobj = re.match(self._VALID_URL, url)
video_id = mobj.group('id')
provider_id = mobj.group('provider_id')
feed_id = mobj.group('feed_id')
filter_query = mobj.group('filter')
return self._extract_feed_info(provider_id, feed_id, filter_query, video_id)
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/formula1.py | youtube_dl/extractor/formula1.py | # coding: utf-8
from __future__ import unicode_literals
from .common import InfoExtractor
class Formula1IE(InfoExtractor):
_VALID_URL = r'https?://(?:www\.)?formula1\.com/en/latest/video\.[^.]+\.(?P<id>\d+)\.html'
_TEST = {
'url': 'https://www.formula1.com/en/latest/video.race-highlights-spain-2016.6060988138001.html',
'md5': 'be7d3a8c2f804eb2ab2aa5d941c359f8',
'info_dict': {
'id': '6060988138001',
'ext': 'mp4',
'title': 'Race highlights - Spain 2016',
'timestamp': 1463332814,
'upload_date': '20160515',
'uploader_id': '6057949432001',
},
'add_ie': ['BrightcoveNew'],
}
BRIGHTCOVE_URL_TEMPLATE = 'http://players.brightcove.net/6057949432001/S1WMrhjlh_default/index.html?videoId=%s'
def _real_extract(self, url):
bc_id = self._match_id(url)
return self.url_result(
self.BRIGHTCOVE_URL_TEMPLATE % bc_id, 'BrightcoveNew', bc_id)
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/canalc2.py | youtube_dl/extractor/canalc2.py | # coding: utf-8
from __future__ import unicode_literals
import re
from .common import InfoExtractor
from ..utils import parse_duration
class Canalc2IE(InfoExtractor):
IE_NAME = 'canalc2.tv'
_VALID_URL = r'https?://(?:(?:www\.)?canalc2\.tv/video/|archives-canalc2\.u-strasbg\.fr/video\.asp\?.*\bidVideo=)(?P<id>\d+)'
_TESTS = [{
'url': 'http://www.canalc2.tv/video/12163',
'md5': '060158428b650f896c542dfbb3d6487f',
'info_dict': {
'id': '12163',
'ext': 'mp4',
'title': 'Terrasses du Numérique',
'duration': 122,
},
}, {
'url': 'http://archives-canalc2.u-strasbg.fr/video.asp?idVideo=11427&voir=oui',
'only_matching': True,
}]
def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(
'http://www.canalc2.tv/video/%s' % video_id, video_id)
title = self._html_search_regex(
r'(?s)class="[^"]*col_description[^"]*">.*?<h3>(.+?)</h3>',
webpage, 'title')
formats = []
for _, video_url in re.findall(r'file\s*=\s*(["\'])(.+?)\1', webpage):
if video_url.startswith('rtmp://'):
rtmp = re.search(
r'^(?P<url>rtmp://[^/]+/(?P<app>.+/))(?P<play_path>mp4:.+)$', video_url)
formats.append({
'url': rtmp.group('url'),
'format_id': 'rtmp',
'ext': 'flv',
'app': rtmp.group('app'),
'play_path': rtmp.group('play_path'),
'page_url': url,
})
else:
formats.append({
'url': video_url,
'format_id': 'http',
})
if formats:
info = {
'formats': formats,
}
else:
info = self._parse_html5_media_entries(url, webpage, url)[0]
self._sort_formats(info['formats'])
info.update({
'id': video_id,
'title': title,
'duration': parse_duration(self._search_regex(
r'id=["\']video_duree["\'][^>]*>([^<]+)',
webpage, 'duration', fatal=False)),
})
return info
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/msn.py | youtube_dl/extractor/msn.py | # coding: utf-8
from __future__ import unicode_literals
import re
from .common import InfoExtractor
from ..compat import compat_str
from ..utils import (
determine_ext,
ExtractorError,
int_or_none,
unescapeHTML,
)
class MSNIE(InfoExtractor):
_VALID_URL = r'https?://(?:(?:www|preview)\.)?msn\.com/(?:[^/]+/)+(?P<display_id>[^/]+)/[a-z]{2}-(?P<id>[\da-zA-Z]+)'
_TESTS = [{
'url': 'https://www.msn.com/en-in/money/video/7-ways-to-get-rid-of-chest-congestion/vi-BBPxU6d',
'md5': '087548191d273c5c55d05028f8d2cbcd',
'info_dict': {
'id': 'BBPxU6d',
'display_id': '7-ways-to-get-rid-of-chest-congestion',
'ext': 'mp4',
'title': 'Seven ways to get rid of chest congestion',
'description': '7 Ways to Get Rid of Chest Congestion',
'duration': 88,
'uploader': 'Health',
'uploader_id': 'BBPrMqa',
},
}, {
# Article, multiple Dailymotion Embeds
'url': 'https://www.msn.com/en-in/money/sports/hottest-football-wags-greatest-footballers-turned-managers-and-more/ar-BBpc7Nl',
'info_dict': {
'id': 'BBpc7Nl',
},
'playlist_mincount': 4,
}, {
'url': 'http://www.msn.com/en-ae/news/offbeat/meet-the-nine-year-old-self-made-millionaire/ar-BBt6ZKf',
'only_matching': True,
}, {
'url': 'http://www.msn.com/en-ae/video/watch/obama-a-lot-of-people-will-be-disappointed/vi-AAhxUMH',
'only_matching': True,
}, {
# geo restricted
'url': 'http://www.msn.com/en-ae/foodanddrink/joinourtable/the-first-fart-makes-you-laugh-the-last-fart-makes-you-cry/vp-AAhzIBU',
'only_matching': True,
}, {
'url': 'http://www.msn.com/en-ae/entertainment/bollywood/watch-how-salman-khan-reacted-when-asked-if-he-would-apologize-for-his-‘raped-woman’-comment/vi-AAhvzW6',
'only_matching': True,
}, {
# Vidible(AOL) Embed
'url': 'https://www.msn.com/en-us/money/other/jupiter-is-about-to-come-so-close-you-can-see-its-moons-with-binoculars/vi-AACqsHR',
'only_matching': True,
}, {
# Dailymotion Embed
'url': 'https://www.msn.com/es-ve/entretenimiento/watch/winston-salem-paire-refait-des-siennes-en-perdant-sa-raquette-au-service/vp-AAG704L',
'only_matching': True,
}, {
# YouTube Embed
'url': 'https://www.msn.com/en-in/money/news/meet-vikram-%E2%80%94-chandrayaan-2s-lander/vi-AAGUr0v',
'only_matching': True,
}, {
# NBCSports Embed
'url': 'https://www.msn.com/en-us/money/football_nfl/week-13-preview-redskins-vs-panthers/vi-BBXsCDb',
'only_matching': True,
}]
def _real_extract(self, url):
display_id, page_id = re.match(self._VALID_URL, url).groups()
webpage = self._download_webpage(url, display_id)
entries = []
for _, metadata in re.findall(r'data-metadata\s*=\s*(["\'])(?P<data>.+?)\1', webpage):
video = self._parse_json(unescapeHTML(metadata), display_id)
provider_id = video.get('providerId')
player_name = video.get('playerName')
if player_name and provider_id:
entry = None
if player_name == 'AOL':
if provider_id.startswith('http'):
provider_id = self._search_regex(
r'https?://delivery\.vidible\.tv/video/redirect/([0-9a-f]{24})',
provider_id, 'vidible id')
entry = self.url_result(
'aol-video:' + provider_id, 'Aol', provider_id)
elif player_name == 'Dailymotion':
entry = self.url_result(
'https://www.dailymotion.com/video/' + provider_id,
'Dailymotion', provider_id)
elif player_name == 'YouTube':
entry = self.url_result(
provider_id, 'Youtube', provider_id)
elif player_name == 'NBCSports':
entry = self.url_result(
'http://vplayer.nbcsports.com/p/BxmELC/nbcsports_embed/select/media/' + provider_id,
'NBCSportsVPlayer', provider_id)
if entry:
entries.append(entry)
continue
video_id = video['uuid']
title = video['title']
formats = []
for file_ in video.get('videoFiles', []):
format_url = file_.get('url')
if not format_url:
continue
if 'format=m3u8-aapl' in format_url:
# m3u8_native should not be used here until
# https://github.com/ytdl-org/youtube-dl/issues/9913 is fixed
formats.extend(self._extract_m3u8_formats(
format_url, display_id, 'mp4',
m3u8_id='hls', fatal=False))
elif 'format=mpd-time-csf' in format_url:
formats.extend(self._extract_mpd_formats(
format_url, display_id, 'dash', fatal=False))
elif '.ism' in format_url:
if format_url.endswith('.ism'):
format_url += '/manifest'
formats.extend(self._extract_ism_formats(
format_url, display_id, 'mss', fatal=False))
else:
format_id = file_.get('formatCode')
formats.append({
'url': format_url,
'ext': 'mp4',
'format_id': format_id,
'width': int_or_none(file_.get('width')),
'height': int_or_none(file_.get('height')),
'vbr': int_or_none(self._search_regex(r'_(\d+)\.mp4', format_url, 'vbr', default=None)),
'preference': 1 if format_id == '1001' else None,
})
self._sort_formats(formats)
subtitles = {}
for file_ in video.get('files', []):
format_url = file_.get('url')
format_code = file_.get('formatCode')
if not format_url or not format_code:
continue
if compat_str(format_code) == '3100':
subtitles.setdefault(file_.get('culture', 'en'), []).append({
'ext': determine_ext(format_url, 'ttml'),
'url': format_url,
})
entries.append({
'id': video_id,
'display_id': display_id,
'title': title,
'description': video.get('description'),
'thumbnail': video.get('headlineImage', {}).get('url'),
'duration': int_or_none(video.get('durationSecs')),
'uploader': video.get('sourceFriendly'),
'uploader_id': video.get('providerId'),
'creator': video.get('creator'),
'subtitles': subtitles,
'formats': formats,
})
if not entries:
error = unescapeHTML(self._search_regex(
r'data-error=(["\'])(?P<error>.+?)\1',
webpage, 'error', group='error'))
raise ExtractorError('%s said: %s' % (self.IE_NAME, error), expected=True)
return self.playlist_result(entries, page_id)
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/tv2hu.py | youtube_dl/extractor/tv2hu.py | # encoding: utf-8
from __future__ import unicode_literals
from .common import InfoExtractor
from ..utils import int_or_none
class TV2HuIE(InfoExtractor):
IE_NAME = 'tv2.hu'
_VALID_URL = r'https?://(?:www\.)?tv2\.hu/(?:[^/]+/)+(?P<id>\d+)_[^/?#]+?\.html'
_TESTS = [{
'url': 'http://tv2.hu/ezek_megorultek/217679_ezek-megorultek---1.-adas-1.-resz.html',
'md5': '585e58e2e090f34603804bb2c48e98d8',
'info_dict': {
'id': '217679',
'ext': 'mp4',
'title': 'Ezek megőrültek! - 1. adás 1. rész',
'upload_date': '20160826',
'thumbnail': r're:^https?://.*\.jpg$'
}
}, {
'url': 'http://tv2.hu/ezek_megorultek/teljes_adasok/217677_ezek-megorultek---1.-adas-2.-resz.html',
'only_matching': True
}, {
'url': 'http://tv2.hu/musoraink/aktiv/aktiv_teljes_adas/217963_aktiv-teljes-adas---2016.08.30..html',
'only_matching': True
}]
def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
json_url = self._search_regex(
r'jsonUrl\s*=\s*"([^"]+)"', webpage, 'json url')
json_data = self._download_json(json_url, video_id)
formats = []
for b in ('bitrates', 'backupBitrates'):
bitrates = json_data.get(b, {})
m3u8_url = bitrates.get('hls')
if m3u8_url:
formats.extend(self._extract_wowza_formats(
m3u8_url, video_id, skip_protocols=['rtmp', 'rtsp']))
for mp4_url in bitrates.get('mp4', []):
height = int_or_none(self._search_regex(
r'\.(\d+)p\.mp4', mp4_url, 'height', default=None))
formats.append({
'format_id': 'http' + ('-%d' % height if height else ''),
'url': mp4_url,
'height': height,
'width': int_or_none(height / 9.0 * 16.0 if height else None),
})
self._sort_formats(formats)
return {
'id': video_id,
'title': self._og_search_title(webpage).strip(),
'thumbnail': self._og_search_thumbnail(webpage),
'upload_date': self._search_regex(
r'/vod/(\d{8})/', json_url, 'upload_date', default=None),
'formats': formats,
}
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/bpb.py | youtube_dl/extractor/bpb.py | # coding: utf-8
from __future__ import unicode_literals
import re
from .common import InfoExtractor
from ..utils import (
js_to_json,
determine_ext,
)
class BpbIE(InfoExtractor):
IE_DESC = 'Bundeszentrale für politische Bildung'
_VALID_URL = r'https?://(?:www\.)?bpb\.de/mediathek/(?P<id>[0-9]+)/'
_TEST = {
'url': 'http://www.bpb.de/mediathek/297/joachim-gauck-zu-1989-und-die-erinnerung-an-die-ddr',
# md5 fails in Python 2.6 due to buggy server response and wrong handling of urllib2
'md5': 'c4f84c8a8044ca9ff68bb8441d300b3f',
'info_dict': {
'id': '297',
'ext': 'mp4',
'title': 'Joachim Gauck zu 1989 und die Erinnerung an die DDR',
'description': 'Joachim Gauck, erster Beauftragter für die Stasi-Unterlagen, spricht auf dem Geschichtsforum über die friedliche Revolution 1989 und eine "gewisse Traurigkeit" im Umgang mit der DDR-Vergangenheit.'
}
}
def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id)
title = self._html_search_regex(
r'<h2 class="white">(.*?)</h2>', webpage, 'title')
video_info_dicts = re.findall(
r"({\s*src\s*:\s*'https?://film\.bpb\.de/[^}]+})", webpage)
formats = []
for video_info in video_info_dicts:
video_info = self._parse_json(
video_info, video_id, transform_source=js_to_json, fatal=False)
if not video_info:
continue
video_url = video_info.get('src')
if not video_url:
continue
quality = 'high' if '_high' in video_url else 'low'
formats.append({
'url': video_url,
'preference': 10 if quality == 'high' else 0,
'format_note': quality,
'format_id': '%s-%s' % (quality, determine_ext(video_url)),
})
self._sort_formats(formats)
return {
'id': video_id,
'formats': formats,
'title': title,
'description': self._og_search_description(webpage),
}
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/rtl2.py | youtube_dl/extractor/rtl2.py | # coding: utf-8
from __future__ import unicode_literals
import re
from .common import InfoExtractor
from ..aes import aes_cbc_decrypt
from ..compat import (
compat_b64decode,
compat_ord,
compat_str,
)
from ..utils import (
bytes_to_intlist,
ExtractorError,
intlist_to_bytes,
int_or_none,
strip_or_none,
)
class RTL2IE(InfoExtractor):
IE_NAME = 'rtl2'
_VALID_URL = r'https?://(?:www\.)?rtl2\.de/sendung/[^/]+/(?:video/(?P<vico_id>\d+)[^/]+/(?P<vivi_id>\d+)-|folge/)(?P<id>[^/?#]+)'
_TESTS = [{
'url': 'http://www.rtl2.de/sendung/grip-das-motormagazin/folge/folge-203-0',
'info_dict': {
'id': 'folge-203-0',
'ext': 'f4v',
'title': 'GRIP sucht den Sommerkönig',
'description': 'md5:e3adbb940fd3c6e76fa341b8748b562f'
},
'params': {
# rtmp download
'skip_download': True,
},
'expected_warnings': ['Unable to download f4m manifest', 'Failed to download m3u8 information'],
}, {
'url': 'http://www.rtl2.de/sendung/koeln-50667/video/5512-anna/21040-anna-erwischt-alex/',
'info_dict': {
'id': 'anna-erwischt-alex',
'ext': 'mp4',
'title': 'Anna erwischt Alex!',
'description': 'Anna nimmt ihrem Vater nicht ab, dass er nicht spielt. Und tatsächlich erwischt sie ihn auf frischer Tat.'
},
'params': {
# rtmp download
'skip_download': True,
},
'expected_warnings': ['Unable to download f4m manifest', 'Failed to download m3u8 information'],
}]
def _real_extract(self, url):
vico_id, vivi_id, display_id = re.match(self._VALID_URL, url).groups()
if not vico_id:
webpage = self._download_webpage(url, display_id)
mobj = re.search(
r'data-collection="(?P<vico_id>\d+)"[^>]+data-video="(?P<vivi_id>\d+)"',
webpage)
if mobj:
vico_id = mobj.group('vico_id')
vivi_id = mobj.group('vivi_id')
else:
vico_id = self._html_search_regex(
r'vico_id\s*:\s*([0-9]+)', webpage, 'vico_id')
vivi_id = self._html_search_regex(
r'vivi_id\s*:\s*([0-9]+)', webpage, 'vivi_id')
info = self._download_json(
'https://service.rtl2.de/api-player-vipo/video.php',
display_id, query={
'vico_id': vico_id,
'vivi_id': vivi_id,
})
video_info = info['video']
title = video_info['titel']
formats = []
rtmp_url = video_info.get('streamurl')
if rtmp_url:
rtmp_url = rtmp_url.replace('\\', '')
stream_url = 'mp4:' + self._html_search_regex(r'/ondemand/(.+)', rtmp_url, 'stream URL')
rtmp_conn = ['S:connect', 'O:1', 'NS:pageUrl:' + url, 'NB:fpad:0', 'NN:videoFunction:1', 'O:0']
formats.append({
'format_id': 'rtmp',
'url': rtmp_url,
'play_path': stream_url,
'player_url': 'https://www.rtl2.de/sites/default/modules/rtl2/jwplayer/jwplayer-7.6.0/jwplayer.flash.swf',
'page_url': url,
'flash_version': 'LNX 11,2,202,429',
'rtmp_conn': rtmp_conn,
'no_resume': True,
'preference': 1,
})
m3u8_url = video_info.get('streamurl_hls')
if m3u8_url:
formats.extend(self._extract_akamai_formats(m3u8_url, display_id))
self._sort_formats(formats)
return {
'id': display_id,
'title': title,
'thumbnail': video_info.get('image'),
'description': video_info.get('beschreibung'),
'duration': int_or_none(video_info.get('duration')),
'formats': formats,
}
class RTL2YouBaseIE(InfoExtractor):
_BACKWERK_BASE_URL = 'https://p-you-backwerk.rtl2apps.de/'
class RTL2YouIE(RTL2YouBaseIE):
IE_NAME = 'rtl2:you'
_VALID_URL = r'http?://you\.rtl2\.de/(?:video/\d+/|youplayer/index\.html\?.*?\bvid=)(?P<id>\d+)'
_TESTS = [{
'url': 'http://you.rtl2.de/video/3002/15740/MJUNIK%20%E2%80%93%20Home%20of%20YOU/307-hirn-wo-bist-du',
'info_dict': {
'id': '15740',
'ext': 'mp4',
'title': 'MJUNIK – Home of YOU - #307 Hirn, wo bist du?!',
'description': 'md5:ddaa95c61b372b12b66e115b2772fe01',
'age_limit': 12,
},
}, {
'url': 'http://you.rtl2.de/youplayer/index.html?vid=15712',
'only_matching': True,
}]
_AES_KEY = b'\xe9W\xe4.<*\xb8\x1a\xd2\xb6\x92\xf3C\xd3\xefL\x1b\x03*\xbbbH\xc0\x03\xffo\xc2\xf2(\xaa\xaa!'
_GEO_COUNTRIES = ['DE']
def _real_extract(self, url):
video_id = self._match_id(url)
stream_data = self._download_json(
self._BACKWERK_BASE_URL + 'stream/video/' + video_id, video_id)
data, iv = compat_b64decode(stream_data['streamUrl']).decode().split(':')
stream_url = intlist_to_bytes(aes_cbc_decrypt(
bytes_to_intlist(compat_b64decode(data)),
bytes_to_intlist(self._AES_KEY),
bytes_to_intlist(compat_b64decode(iv))
))
if b'rtl2_you_video_not_found' in stream_url:
raise ExtractorError('video not found', expected=True)
formats = self._extract_m3u8_formats(
stream_url[:-compat_ord(stream_url[-1])].decode(),
video_id, 'mp4', 'm3u8_native')
self._sort_formats(formats)
video_data = self._download_json(
self._BACKWERK_BASE_URL + 'video/' + video_id, video_id)
series = video_data.get('formatTitle')
title = episode = video_data.get('title') or series
if series and series != title:
title = '%s - %s' % (series, title)
return {
'id': video_id,
'title': title,
'formats': formats,
'description': strip_or_none(video_data.get('description')),
'thumbnail': video_data.get('image'),
'duration': int_or_none(stream_data.get('duration') or video_data.get('duration'), 1000),
'series': series,
'episode': episode,
'age_limit': int_or_none(video_data.get('minimumAge')),
}
class RTL2YouSeriesIE(RTL2YouBaseIE):
IE_NAME = 'rtl2:you:series'
_VALID_URL = r'http?://you\.rtl2\.de/videos/(?P<id>\d+)'
_TEST = {
'url': 'http://you.rtl2.de/videos/115/dragon-ball',
'info_dict': {
'id': '115',
},
'playlist_mincount': 5,
}
def _real_extract(self, url):
series_id = self._match_id(url)
stream_data = self._download_json(
self._BACKWERK_BASE_URL + 'videos',
series_id, query={
'formatId': series_id,
'limit': 1000000000,
})
entries = []
for video in stream_data.get('videos', []):
video_id = compat_str(video['videoId'])
if not video_id:
continue
entries.append(self.url_result(
'http://you.rtl2.de/video/%s/%s' % (series_id, video_id),
'RTL2You', video_id))
return self.playlist_result(entries, series_id)
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/matchtv.py | youtube_dl/extractor/matchtv.py | # coding: utf-8
from __future__ import unicode_literals
import random
from .common import InfoExtractor
from ..utils import xpath_text
class MatchTVIE(InfoExtractor):
_VALID_URL = r'https?://matchtv\.ru(?:/on-air|/?#live-player)'
_TESTS = [{
'url': 'http://matchtv.ru/#live-player',
'info_dict': {
'id': 'matchtv-live',
'ext': 'flv',
'title': r're:^Матч ТВ - Прямой эфир \d{4}-\d{2}-\d{2} \d{2}:\d{2}$',
'is_live': True,
},
'params': {
'skip_download': True,
},
}, {
'url': 'http://matchtv.ru/on-air/',
'only_matching': True,
}]
def _real_extract(self, url):
video_id = 'matchtv-live'
video_url = self._download_json(
'http://player.matchtv.ntvplus.tv/player/smil', video_id,
query={
'ts': '',
'quality': 'SD',
'contentId': '561d2c0df7159b37178b4567',
'sign': '',
'includeHighlights': '0',
'userId': '',
'sessionId': random.randint(1, 1000000000),
'contentType': 'channel',
'timeShift': '0',
'platform': 'portal',
},
headers={
'Referer': 'http://player.matchtv.ntvplus.tv/embed-player/NTVEmbedPlayer.swf',
})['data']['videoUrl']
f4m_url = xpath_text(self._download_xml(video_url, video_id), './to')
formats = self._extract_f4m_formats(f4m_url, video_id)
self._sort_formats(formats)
return {
'id': video_id,
'title': self._live_title('Матч ТВ - Прямой эфир'),
'is_live': True,
'formats': formats,
}
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/blerp.py | youtube_dl/extractor/blerp.py | # coding: utf-8
from __future__ import unicode_literals
import json
from ..utils import (
strip_or_none,
traverse_obj,
)
from .common import InfoExtractor
class BlerpIE(InfoExtractor):
IE_NAME = 'blerp'
_VALID_URL = r'https?://(?:www\.)?blerp\.com/soundbites/(?P<id>[0-9a-zA-Z]+)'
_TESTS = [{
'url': 'https://blerp.com/soundbites/6320fe8745636cb4dd677a5a',
'info_dict': {
'id': '6320fe8745636cb4dd677a5a',
'title': 'Samsung Galaxy S8 Over the Horizon Ringtone 2016',
'uploader': 'luminousaj',
'uploader_id': '5fb81e51aa66ae000c395478',
'ext': 'mp3',
'tags': ['samsung', 'galaxy', 's8', 'over the horizon', '2016', 'ringtone'],
}
}, {
'url': 'https://blerp.com/soundbites/5bc94ef4796001000498429f',
'info_dict': {
'id': '5bc94ef4796001000498429f',
'title': 'Yee',
'uploader': '179617322678353920',
'uploader_id': '5ba99cf71386730004552c42',
'ext': 'mp3',
'tags': ['YEE', 'YEET', 'wo ha haah catchy tune yee', 'yee']
}
}]
_GRAPHQL_OPERATIONNAME = "webBitePageGetBite"
_GRAPHQL_QUERY = (
'''query webBitePageGetBite($_id: MongoID!) {
web {
biteById(_id: $_id) {
...bitePageFrag
__typename
}
__typename
}
}
fragment bitePageFrag on Bite {
_id
title
userKeywords
keywords
color
visibility
isPremium
owned
price
extraReview
isAudioExists
image {
filename
original {
url
__typename
}
__typename
}
userReactions {
_id
reactions
createdAt
__typename
}
topReactions
totalSaveCount
saved
blerpLibraryType
license
licenseMetaData
playCount
totalShareCount
totalFavoriteCount
totalAddedToBoardCount
userCategory
userAudioQuality
audioCreationState
transcription
userTranscription
description
createdAt
updatedAt
author
listingType
ownerObject {
_id
username
profileImage {
filename
original {
url
__typename
}
__typename
}
__typename
}
transcription
favorited
visibility
isCurated
sourceUrl
audienceRating
strictAudienceRating
ownerId
reportObject {
reportedContentStatus
__typename
}
giphy {
mp4
gif
__typename
}
audio {
filename
original {
url
__typename
}
mp3 {
url
__typename
}
__typename
}
__typename
}
''')
def _real_extract(self, url):
audio_id = self._match_id(url)
data = {
'operationName': self._GRAPHQL_OPERATIONNAME,
'query': self._GRAPHQL_QUERY,
'variables': {
'_id': audio_id
}
}
headers = {
'Content-Type': 'application/json'
}
json_result = self._download_json('https://api.blerp.com/graphql',
audio_id, data=json.dumps(data).encode('utf-8'), headers=headers)
bite_json = json_result['data']['web']['biteById']
info_dict = {
'id': bite_json['_id'],
'url': bite_json['audio']['mp3']['url'],
'title': bite_json['title'],
'uploader': traverse_obj(bite_json, ('ownerObject', 'username'), expected_type=strip_or_none),
'uploader_id': traverse_obj(bite_json, ('ownerObject', '_id'), expected_type=strip_or_none),
'ext': 'mp3',
'tags': list(filter(None, map(strip_or_none, (traverse_obj(bite_json, 'userKeywords', expected_type=list) or []))) or None)
}
return info_dict
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/sbs.py | youtube_dl/extractor/sbs.py | # coding: utf-8
from __future__ import unicode_literals
from .common import InfoExtractor
from ..utils import (
smuggle_url,
ExtractorError,
)
class SBSIE(InfoExtractor):
IE_DESC = 'sbs.com.au'
_VALID_URL = r'https?://(?:www\.)?sbs\.com\.au/(?:ondemand(?:/video/(?:single/)?|.*?\bplay=|/watch/)|news/(?:embeds/)?video/)(?P<id>[0-9]+)'
_TESTS = [{
# Original URL is handled by the generic IE which finds the iframe:
# http://www.sbs.com.au/thefeed/blog/2014/08/21/dingo-conservation
'url': 'http://www.sbs.com.au/ondemand/video/single/320403011771/?source=drupal&vertical=thefeed',
'md5': '3150cf278965eeabb5b4cea1c963fe0a',
'info_dict': {
'id': '_rFBPRPO4pMR',
'ext': 'mp4',
'title': 'Dingo Conservation (The Feed)',
'description': 'md5:f250a9856fca50d22dec0b5b8015f8a5',
'thumbnail': r're:http://.*\.jpg',
'duration': 308,
'timestamp': 1408613220,
'upload_date': '20140821',
'uploader': 'SBSC',
},
}, {
'url': 'http://www.sbs.com.au/ondemand/video/320403011771/Dingo-Conservation-The-Feed',
'only_matching': True,
}, {
'url': 'http://www.sbs.com.au/news/video/471395907773/The-Feed-July-9',
'only_matching': True,
}, {
'url': 'https://www.sbs.com.au/ondemand/?play=1836638787723',
'only_matching': True,
}, {
'url': 'https://www.sbs.com.au/ondemand/program/inside-windsor-castle?play=1283505731842',
'only_matching': True,
}, {
'url': 'https://www.sbs.com.au/news/embeds/video/1840778819866',
'only_matching': True,
}, {
'url': 'https://www.sbs.com.au/ondemand/watch/1698704451971',
'only_matching': True,
}]
def _real_extract(self, url):
video_id = self._match_id(url)
player_params = self._download_json(
'http://www.sbs.com.au/api/video_pdkvars/id/%s?form=json' % video_id, video_id)
error = player_params.get('error')
if error:
error_message = 'Sorry, The video you are looking for does not exist.'
video_data = error.get('results') or {}
error_code = error.get('errorCode')
if error_code == 'ComingSoon':
error_message = '%s is not yet available.' % video_data.get('title', '')
elif error_code in ('Forbidden', 'intranetAccessOnly'):
error_message = 'Sorry, This video cannot be accessed via this website'
elif error_code == 'Expired':
error_message = 'Sorry, %s is no longer available.' % video_data.get('title', '')
raise ExtractorError('%s said: %s' % (self.IE_NAME, error_message), expected=True)
urls = player_params['releaseUrls']
theplatform_url = (urls.get('progressive') or urls.get('html')
or urls.get('standard') or player_params['relatedItemsURL'])
return {
'_type': 'url_transparent',
'ie_key': 'ThePlatform',
'id': video_id,
'url': smuggle_url(self._proto_relative_url(theplatform_url), {'force_smil_url': True}),
}
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/pyvideo.py | youtube_dl/extractor/pyvideo.py | from __future__ import unicode_literals
import re
from .common import InfoExtractor
from ..compat import compat_str
from ..utils import int_or_none
class PyvideoIE(InfoExtractor):
_VALID_URL = r'https?://(?:www\.)?pyvideo\.org/(?P<category>[^/]+)/(?P<id>[^/?#&.]+)'
_TESTS = [{
'url': 'http://pyvideo.org/pycon-us-2013/become-a-logging-expert-in-30-minutes.html',
'info_dict': {
'id': 'become-a-logging-expert-in-30-minutes',
},
'playlist_count': 2,
}, {
'url': 'http://pyvideo.org/pygotham-2012/gloriajw-spotifywitherikbernhardsson182m4v.html',
'md5': '5fe1c7e0a8aa5570330784c847ff6d12',
'info_dict': {
'id': '2542',
'ext': 'm4v',
'title': 'Gloriajw-SpotifyWithErikBernhardsson182.m4v',
},
}]
def _real_extract(self, url):
mobj = re.match(self._VALID_URL, url)
category = mobj.group('category')
video_id = mobj.group('id')
entries = []
data = self._download_json(
'https://raw.githubusercontent.com/pyvideo/data/master/%s/videos/%s.json'
% (category, video_id), video_id, fatal=False)
if data:
for video in data['videos']:
video_url = video.get('url')
if video_url:
if video.get('type') == 'youtube':
entries.append(self.url_result(video_url, 'Youtube'))
else:
entries.append({
'id': compat_str(data.get('id') or video_id),
'url': video_url,
'title': data['title'],
'description': data.get('description') or data.get('summary'),
'thumbnail': data.get('thumbnail_url'),
'duration': int_or_none(data.get('duration')),
})
else:
webpage = self._download_webpage(url, video_id)
title = self._og_search_title(webpage)
media_urls = self._search_regex(
r'(?s)Media URL:(.+?)</li>', webpage, 'media urls')
for m in re.finditer(
r'<a[^>]+href=(["\'])(?P<url>http.+?)\1', media_urls):
media_url = m.group('url')
if re.match(r'https?://www\.youtube\.com/watch\?v=.*', media_url):
entries.append(self.url_result(media_url, 'Youtube'))
else:
entries.append({
'id': video_id,
'url': media_url,
'title': title,
})
return self.playlist_result(entries, video_id)
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/awaan.py | youtube_dl/extractor/awaan.py | # coding: utf-8
from __future__ import unicode_literals
import re
import base64
from .common import InfoExtractor
from ..compat import (
compat_urllib_parse_urlencode,
compat_str,
)
from ..utils import (
int_or_none,
parse_iso8601,
smuggle_url,
unsmuggle_url,
urlencode_postdata,
)
class AWAANIE(InfoExtractor):
_VALID_URL = r'https?://(?:www\.)?(?:awaan|dcndigital)\.ae/(?:#/)?show/(?P<show_id>\d+)/[^/]+(?:/(?P<video_id>\d+)/(?P<season_id>\d+))?'
def _real_extract(self, url):
show_id, video_id, season_id = re.match(self._VALID_URL, url).groups()
if video_id and int(video_id) > 0:
return self.url_result(
'http://awaan.ae/media/%s' % video_id, 'AWAANVideo')
elif season_id and int(season_id) > 0:
return self.url_result(smuggle_url(
'http://awaan.ae/program/season/%s' % season_id,
{'show_id': show_id}), 'AWAANSeason')
else:
return self.url_result(
'http://awaan.ae/program/%s' % show_id, 'AWAANSeason')
class AWAANBaseIE(InfoExtractor):
def _parse_video_data(self, video_data, video_id, is_live):
title = video_data.get('title_en') or video_data['title_ar']
img = video_data.get('img')
return {
'id': video_id,
'title': self._live_title(title) if is_live else title,
'description': video_data.get('description_en') or video_data.get('description_ar'),
'thumbnail': 'http://admin.mangomolo.com/analytics/%s' % img if img else None,
'duration': int_or_none(video_data.get('duration')),
'timestamp': parse_iso8601(video_data.get('create_time'), ' '),
'is_live': is_live,
'uploader_id': video_data.get('user_id'),
}
class AWAANVideoIE(AWAANBaseIE):
IE_NAME = 'awaan:video'
_VALID_URL = r'https?://(?:www\.)?(?:awaan|dcndigital)\.ae/(?:#/)?(?:video(?:/[^/]+)?|media|catchup/[^/]+/[^/]+)/(?P<id>\d+)'
_TESTS = [{
'url': 'http://www.dcndigital.ae/#/video/%D8%B1%D8%AD%D9%84%D8%A9-%D8%A7%D9%84%D8%B9%D9%85%D8%B1-%D8%A7%D9%84%D8%AD%D9%84%D9%82%D8%A9-1/17375',
'md5': '5f61c33bfc7794315c671a62d43116aa',
'info_dict':
{
'id': '17375',
'ext': 'mp4',
'title': 'رحلة العمر : الحلقة 1',
'description': 'md5:0156e935d870acb8ef0a66d24070c6d6',
'duration': 2041,
'timestamp': 1227504126,
'upload_date': '20081124',
'uploader_id': '71',
},
}, {
'url': 'http://awaan.ae/video/26723981/%D8%AF%D8%A7%D8%B1-%D8%A7%D9%84%D8%B3%D9%84%D8%A7%D9%85:-%D8%AE%D9%8A%D8%B1-%D8%AF%D9%88%D8%B1-%D8%A7%D9%84%D8%A3%D9%86%D8%B5%D8%A7%D8%B1',
'only_matching': True,
}]
def _real_extract(self, url):
video_id = self._match_id(url)
video_data = self._download_json(
'http://admin.mangomolo.com/analytics/index.php/plus/video?id=%s' % video_id,
video_id, headers={'Origin': 'http://awaan.ae'})
info = self._parse_video_data(video_data, video_id, False)
embed_url = 'http://admin.mangomolo.com/analytics/index.php/customers/embed/video?' + compat_urllib_parse_urlencode({
'id': video_data['id'],
'user_id': video_data['user_id'],
'signature': video_data['signature'],
'countries': 'Q0M=',
'filter': 'DENY',
})
info.update({
'_type': 'url_transparent',
'url': embed_url,
'ie_key': 'MangomoloVideo',
})
return info
class AWAANLiveIE(AWAANBaseIE):
IE_NAME = 'awaan:live'
_VALID_URL = r'https?://(?:www\.)?(?:awaan|dcndigital)\.ae/(?:#/)?live/(?P<id>\d+)'
_TEST = {
'url': 'http://awaan.ae/live/6/dubai-tv',
'info_dict': {
'id': '6',
'ext': 'mp4',
'title': 're:Dubai Al Oula [0-9]{4}-[0-9]{2}-[0-9]{2} [0-9]{2}:[0-9]{2}$',
'upload_date': '20150107',
'timestamp': 1420588800,
'uploader_id': '71',
},
'params': {
# m3u8 download
'skip_download': True,
},
}
def _real_extract(self, url):
channel_id = self._match_id(url)
channel_data = self._download_json(
'http://admin.mangomolo.com/analytics/index.php/plus/getchanneldetails?channel_id=%s' % channel_id,
channel_id, headers={'Origin': 'http://awaan.ae'})
info = self._parse_video_data(channel_data, channel_id, True)
embed_url = 'http://admin.mangomolo.com/analytics/index.php/customers/embed/index?' + compat_urllib_parse_urlencode({
'id': base64.b64encode(channel_data['user_id'].encode()).decode(),
'channelid': base64.b64encode(channel_data['id'].encode()).decode(),
'signature': channel_data['signature'],
'countries': 'Q0M=',
'filter': 'DENY',
})
info.update({
'_type': 'url_transparent',
'url': embed_url,
'ie_key': 'MangomoloLive',
})
return info
class AWAANSeasonIE(InfoExtractor):
IE_NAME = 'awaan:season'
_VALID_URL = r'https?://(?:www\.)?(?:awaan|dcndigital)\.ae/(?:#/)?program/(?:(?P<show_id>\d+)|season/(?P<season_id>\d+))'
_TEST = {
'url': 'http://dcndigital.ae/#/program/205024/%D9%85%D8%AD%D8%A7%D8%B6%D8%B1%D8%A7%D8%AA-%D8%A7%D9%84%D8%B4%D9%8A%D8%AE-%D8%A7%D9%84%D8%B4%D8%B9%D8%B1%D8%A7%D9%88%D9%8A',
'info_dict':
{
'id': '7910',
'title': 'محاضرات الشيخ الشعراوي',
},
'playlist_mincount': 27,
}
def _real_extract(self, url):
url, smuggled_data = unsmuggle_url(url, {})
show_id, season_id = re.match(self._VALID_URL, url).groups()
data = {}
if season_id:
data['season'] = season_id
show_id = smuggled_data.get('show_id')
if show_id is None:
season = self._download_json(
'http://admin.mangomolo.com/analytics/index.php/plus/season_info?id=%s' % season_id,
season_id, headers={'Origin': 'http://awaan.ae'})
show_id = season['id']
data['show_id'] = show_id
show = self._download_json(
'http://admin.mangomolo.com/analytics/index.php/plus/show',
show_id, data=urlencode_postdata(data), headers={
'Origin': 'http://awaan.ae',
'Content-Type': 'application/x-www-form-urlencoded'
})
if not season_id:
season_id = show['default_season']
for season in show['seasons']:
if season['id'] == season_id:
title = season.get('title_en') or season['title_ar']
entries = []
for video in show['videos']:
video_id = compat_str(video['id'])
entries.append(self.url_result(
'http://awaan.ae/media/%s' % video_id, 'AWAANVideo', video_id))
return self.playlist_result(entries, season_id, title)
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/sina.py | youtube_dl/extractor/sina.py | # coding: utf-8
from __future__ import unicode_literals
import re
from .common import InfoExtractor
from ..utils import (
HEADRequest,
ExtractorError,
int_or_none,
update_url_query,
qualities,
get_element_by_attribute,
clean_html,
)
class SinaIE(InfoExtractor):
_VALID_URL = r'''(?x)https?://(?:.*?\.)?video\.sina\.com\.cn/
(?:
(?:view/|.*\#)(?P<video_id>\d+)|
.+?/(?P<pseudo_id>[^/?#]+)(?:\.s?html)|
# This is used by external sites like Weibo
api/sinawebApi/outplay.php/(?P<token>.+?)\.swf
)
'''
_TESTS = [
{
'url': 'http://video.sina.com.cn/news/spj/topvideoes20160504/?opsubject_id=top1#250576622',
'md5': 'd38433e2fc886007729735650ae4b3e9',
'info_dict': {
'id': '250576622',
'ext': 'mp4',
'title': '现场:克鲁兹宣布退选 特朗普将稳获提名',
}
},
{
'url': 'http://video.sina.com.cn/v/b/101314253-1290078633.html',
'info_dict': {
'id': '101314253',
'ext': 'flv',
'title': '军方提高对朝情报监视级别',
},
'skip': 'the page does not exist or has been deleted',
},
{
'url': 'http://video.sina.com.cn/view/250587748.html',
'md5': '3d1807a25c775092aab3bc157fff49b4',
'info_dict': {
'id': '250587748',
'ext': 'mp4',
'title': '瞬间泪目：8年前汶川地震珍贵视频首曝光',
},
},
]
def _real_extract(self, url):
mobj = re.match(self._VALID_URL, url)
video_id = mobj.group('video_id')
if not video_id:
if mobj.group('token') is not None:
# The video id is in the redirected url
self.to_screen('Getting video id')
request = HEADRequest(url)
_, urlh = self._download_webpage_handle(request, 'NA', False)
return self._real_extract(urlh.geturl())
else:
pseudo_id = mobj.group('pseudo_id')
webpage = self._download_webpage(url, pseudo_id)
error = get_element_by_attribute('class', 'errtitle', webpage)
if error:
raise ExtractorError('%s said: %s' % (
self.IE_NAME, clean_html(error)), expected=True)
video_id = self._search_regex(
r"video_id\s*:\s*'(\d+)'", webpage, 'video id')
video_data = self._download_json(
'http://s.video.sina.com.cn/video/h5play',
video_id, query={'video_id': video_id})
if video_data['code'] != 1:
raise ExtractorError('%s said: %s' % (
self.IE_NAME, video_data['message']), expected=True)
else:
video_data = video_data['data']
title = video_data['title']
description = video_data.get('description')
if description:
description = description.strip()
preference = qualities(['cif', 'sd', 'hd', 'fhd', 'ffd'])
formats = []
for quality_id, quality in video_data.get('videos', {}).get('mp4', {}).items():
file_api = quality.get('file_api')
file_id = quality.get('file_id')
if not file_api or not file_id:
continue
formats.append({
'format_id': quality_id,
'url': update_url_query(file_api, {'vid': file_id}),
'preference': preference(quality_id),
'ext': 'mp4',
})
self._sort_formats(formats)
return {
'id': video_id,
'title': title,
'description': description,
'thumbnail': video_data.get('image'),
'duration': int_or_none(video_data.get('length')),
'timestamp': int_or_none(video_data.get('create_time')),
'formats': formats,
}
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/caffeine.py | youtube_dl/extractor/caffeine.py | # coding: utf-8
from __future__ import unicode_literals
from .common import InfoExtractor
from ..utils import (
determine_ext,
int_or_none,
merge_dicts,
parse_iso8601,
T,
traverse_obj,
txt_or_none,
urljoin,
)
class CaffeineTVIE(InfoExtractor):
_VALID_URL = r'https?://(?:www\.)?caffeine\.tv/[^/]+/video/(?P<id>[0-9a-f-]+)'
_TESTS = [{
'url': 'https://www.caffeine.tv/TsuSurf/video/cffc0a00-e73f-11ec-8080-80017d29f26e',
'info_dict': {
'id': 'cffc0a00-e73f-11ec-8080-80017d29f26e',
'ext': 'mp4',
'title': 'GOOOOD MORNINNNNN #highlights',
'timestamp': 1654702180,
'upload_date': '20220608',
'uploader': 'TsuSurf',
'duration': 3145,
'age_limit': 17,
},
'params': {
'format': 'bestvideo',
},
}]
def _real_extract(self, url):
video_id = self._match_id(url)
json_data = self._download_json(
'https://api.caffeine.tv/social/public/activity/' + video_id,
video_id)
broadcast_info = traverse_obj(json_data, ('broadcast_info', T(dict))) or {}
title = broadcast_info['broadcast_title']
video_url = broadcast_info['video_url']
ext = determine_ext(video_url)
if ext == 'm3u8':
formats = self._extract_m3u8_formats(
video_url, video_id, 'mp4', entry_protocol='m3u8',
fatal=False)
else:
formats = [{'url': video_url}]
self._sort_formats(formats)
return merge_dicts({
'id': video_id,
'title': title,
'formats': formats,
}, traverse_obj(json_data, {
'uploader': ((None, 'user'), 'username'),
}, get_all=False), traverse_obj(json_data, {
'like_count': ('like_count', T(int_or_none)),
'view_count': ('view_count', T(int_or_none)),
'comment_count': ('comment_count', T(int_or_none)),
'tags': ('tags', Ellipsis, T(txt_or_none)),
'is_live': 'is_live',
'uploader': ('user', 'name'),
}), traverse_obj(broadcast_info, {
'duration': ('content_duration', T(int_or_none)),
'timestamp': ('broadcast_start_time', T(parse_iso8601)),
'thumbnail': ('preview_image_path', T(lambda u: urljoin(url, u))),
'age_limit': ('content_rating', T(lambda r: r and {
# assume Apple Store ratings [1]
# 1. https://en.wikipedia.org/wiki/Mobile_software_content_rating_system
'FOUR_PLUS': 0,
'NINE_PLUS': 9,
'TWELVE_PLUS': 12,
'SEVENTEEN_PLUS': 17,
}.get(r, 17))),
}))
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/spankbang.py | youtube_dl/extractor/spankbang.py | from __future__ import unicode_literals
import re
from .common import InfoExtractor
from ..utils import (
determine_ext,
ExtractorError,
merge_dicts,
parse_duration,
parse_resolution,
str_to_int,
url_or_none,
urlencode_postdata,
urljoin,
)
class SpankBangIE(InfoExtractor):
_VALID_URL = r'''(?x)
https?://
(?:[^/]+\.)?spankbang\.com/
(?:
(?P<id>[\da-z]+)/(?:video|play|embed)\b|
[\da-z]+-(?P<id_2>[\da-z]+)/playlist/[^/?#&]+
)
'''
_TESTS = [{
'url': 'http://spankbang.com/3vvn/video/fantasy+solo',
'md5': '1cc433e1d6aa14bc376535b8679302f7',
'info_dict': {
'id': '3vvn',
'ext': 'mp4',
'title': 'fantasy solo',
'description': 'dillion harper masturbates on a bed',
'thumbnail': r're:^https?://.*\.jpg$',
'uploader': 'silly2587',
'timestamp': 1422571989,
'upload_date': '20150129',
'age_limit': 18,
}
}, {
# 480p only
'url': 'http://spankbang.com/1vt0/video/solvane+gangbang',
'only_matching': True,
}, {
# no uploader
'url': 'http://spankbang.com/lklg/video/sex+with+anyone+wedding+edition+2',
'only_matching': True,
}, {
# mobile page
'url': 'http://m.spankbang.com/1o2de/video/can+t+remember+her+name',
'only_matching': True,
}, {
# 4k
'url': 'https://spankbang.com/1vwqx/video/jade+kush+solo+4k',
'only_matching': True,
}, {
'url': 'https://m.spankbang.com/3vvn/play/fantasy+solo/480p/',
'only_matching': True,
}, {
'url': 'https://m.spankbang.com/3vvn/play',
'only_matching': True,
}, {
'url': 'https://spankbang.com/2y3td/embed/',
'only_matching': True,
}, {
'url': 'https://spankbang.com/2v7ik-7ecbgu/playlist/latina+booty',
'only_matching': True,
}]
def _real_extract(self, url):
mobj = re.match(self._VALID_URL, url)
video_id = mobj.group('id') or mobj.group('id_2')
webpage = self._download_webpage(
url.replace('/%s/embed' % video_id, '/%s/video' % video_id),
video_id, headers={'Cookie': 'country=US'})
if re.search(r'<[^>]+\b(?:id|class)=["\']video_removed', webpage):
raise ExtractorError(
'Video %s is not available' % video_id, expected=True)
formats = []
def extract_format(format_id, format_url):
f_url = url_or_none(format_url)
if not f_url:
return
f = parse_resolution(format_id)
ext = determine_ext(f_url)
if format_id.startswith('m3u8') or ext == 'm3u8':
formats.extend(self._extract_m3u8_formats(
f_url, video_id, 'mp4', entry_protocol='m3u8_native',
m3u8_id='hls', fatal=False))
elif format_id.startswith('mpd') or ext == 'mpd':
formats.extend(self._extract_mpd_formats(
f_url, video_id, mpd_id='dash', fatal=False))
elif ext == 'mp4' or f.get('width') or f.get('height'):
f.update({
'url': f_url,
'format_id': format_id,
})
formats.append(f)
STREAM_URL_PREFIX = 'stream_url_'
for mobj in re.finditer(
r'%s(?P<id>[^\s=]+)\s*=\s*(["\'])(?P<url>(?:(?!\2).)+)\2'
% STREAM_URL_PREFIX, webpage):
extract_format(mobj.group('id', 'url'))
if not formats:
stream_key = self._search_regex(
r'data-streamkey\s*=\s*(["\'])(?P<value>(?:(?!\1).)+)\1',
webpage, 'stream key', group='value')
stream = self._download_json(
'https://spankbang.com/api/videos/stream', video_id,
'Downloading stream JSON', data=urlencode_postdata({
'id': stream_key,
'data': 0,
}), headers={
'Referer': url,
'X-Requested-With': 'XMLHttpRequest',
})
for format_id, format_url in stream.items():
if format_url and isinstance(format_url, list):
format_url = format_url[0]
extract_format(format_id, format_url)
self._sort_formats(formats, field_preference=('preference', 'height', 'width', 'fps', 'tbr', 'format_id'))
info = self._search_json_ld(webpage, video_id, default={})
title = self._html_search_regex(
r'(?s)<h1[^>]*>(.+?)</h1>', webpage, 'title', default=None)
description = self._search_regex(
r'<div[^>]+\bclass=["\']bottom[^>]+>\s*<p>[^<]*</p>\s*<p>([^<]+)',
webpage, 'description', default=None)
thumbnail = self._og_search_thumbnail(webpage, default=None)
uploader = self._html_search_regex(
(r'(?s)<li[^>]+class=["\']profile[^>]+>(.+?)</a>',
r'class="user"[^>]*><img[^>]+>([^<]+)'),
webpage, 'uploader', default=None)
duration = parse_duration(self._search_regex(
r'<div[^>]+\bclass=["\']right_side[^>]+>\s*<span>([^<]+)',
webpage, 'duration', default=None))
view_count = str_to_int(self._search_regex(
r'([\d,.]+)\s+plays', webpage, 'view count', default=None))
age_limit = self._rta_search(webpage)
return merge_dicts({
'id': video_id,
'title': title or video_id,
'description': description,
'thumbnail': thumbnail,
'uploader': uploader,
'duration': duration,
'view_count': view_count,
'formats': formats,
'age_limit': age_limit,
}, info
)
class SpankBangPlaylistIE(InfoExtractor):
_VALID_URL = r'https?://(?:[^/]+\.)?spankbang\.com/(?P<id>[\da-z]+)/playlist/(?P<display_id>[^/]+)'
_TEST = {
'url': 'https://spankbang.com/ug0k/playlist/big+ass+titties',
'info_dict': {
'id': 'ug0k',
'title': 'Big Ass Titties',
},
'playlist_mincount': 40,
}
def _real_extract(self, url):
mobj = re.match(self._VALID_URL, url)
playlist_id = mobj.group('id')
display_id = mobj.group('display_id')
webpage = self._download_webpage(
url, playlist_id, headers={'Cookie': 'country=US; mobile=on'})
entries = [self.url_result(
urljoin(url, mobj.group('path')),
ie=SpankBangIE.ie_key(), video_id=mobj.group('id'))
for mobj in re.finditer(
r'<a[^>]+\bhref=(["\'])(?P<path>/?[\da-z]+-(?P<id>[\da-z]+)/playlist/%s(?:(?!\1).)*)\1'
% re.escape(display_id), webpage)]
title = self._html_search_regex(
r'<h1>([^<]+)\s+playlist\s*<', webpage, 'playlist title',
fatal=False)
return self.playlist_result(entries, playlist_id, title)
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/popcorntv.py | youtube_dl/extractor/popcorntv.py | from __future__ import unicode_literals
import re
from .common import InfoExtractor
from ..utils import (
extract_attributes,
int_or_none,
unified_timestamp,
)
class PopcornTVIE(InfoExtractor):
_VALID_URL = r'https?://[^/]+\.popcorntv\.it/guarda/(?P<display_id>[^/]+)/(?P<id>\d+)'
_TESTS = [{
'url': 'https://animemanga.popcorntv.it/guarda/food-wars-battaglie-culinarie-episodio-01/9183',
'md5': '47d65a48d147caf692ab8562fe630b45',
'info_dict': {
'id': '9183',
'display_id': 'food-wars-battaglie-culinarie-episodio-01',
'ext': 'mp4',
'title': 'Food Wars, Battaglie Culinarie | Episodio 01',
'description': 'md5:b8bea378faae4651d3b34c6e112463d0',
'thumbnail': r're:^https?://.*\.jpg$',
'timestamp': 1497610857,
'upload_date': '20170616',
'duration': 1440,
'view_count': int,
},
}, {
'url': 'https://cinema.popcorntv.it/guarda/smash-cut/10433',
'only_matching': True,
}]
def _real_extract(self, url):
mobj = re.match(self._VALID_URL, url)
display_id, video_id = mobj.group('display_id', 'id')
webpage = self._download_webpage(url, display_id)
m3u8_url = extract_attributes(
self._search_regex(
r'(<link[^>]+itemprop=["\'](?:content|embed)Url[^>]*>)',
webpage, 'content'
))['href']
formats = self._extract_m3u8_formats(
m3u8_url, display_id, 'mp4', entry_protocol='m3u8_native',
m3u8_id='hls')
title = self._search_regex(
r'<h1[^>]+itemprop=["\']name[^>]*>([^<]+)', webpage,
'title', default=None) or self._og_search_title(webpage)
description = self._html_search_regex(
r'(?s)<article[^>]+itemprop=["\']description[^>]*>(.+?)</article>',
webpage, 'description', fatal=False)
thumbnail = self._og_search_thumbnail(webpage)
timestamp = unified_timestamp(self._html_search_meta(
'uploadDate', webpage, 'timestamp'))
duration = int_or_none(self._html_search_meta(
'duration', webpage), invscale=60)
view_count = int_or_none(self._html_search_meta(
'interactionCount', webpage, 'view count'))
return {
'id': video_id,
'display_id': display_id,
'title': title,
'description': description,
'thumbnail': thumbnail,
'timestamp': timestamp,
'duration': duration,
'view_count': view_count,
'formats': formats,
}
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/acast.py | youtube_dl/extractor/acast.py | # coding: utf-8
from __future__ import unicode_literals
import re
from .common import InfoExtractor
from ..utils import (
clean_html,
clean_podcast_url,
int_or_none,
parse_iso8601,
)
class ACastBaseIE(InfoExtractor):
def _extract_episode(self, episode, show_info):
title = episode['title']
info = {
'id': episode['id'],
'display_id': episode.get('episodeUrl'),
'url': clean_podcast_url(episode['url']),
'title': title,
'description': clean_html(episode.get('description') or episode.get('summary')),
'thumbnail': episode.get('image'),
'timestamp': parse_iso8601(episode.get('publishDate')),
'duration': int_or_none(episode.get('duration')),
'filesize': int_or_none(episode.get('contentLength')),
'season_number': int_or_none(episode.get('season')),
'episode': title,
'episode_number': int_or_none(episode.get('episode')),
}
info.update(show_info)
return info
def _extract_show_info(self, show):
return {
'creator': show.get('author'),
'series': show.get('title'),
}
def _call_api(self, path, video_id, query=None):
return self._download_json(
'https://feeder.acast.com/api/v1/shows/' + path, video_id, query=query)
class ACastIE(ACastBaseIE):
IE_NAME = 'acast'
_VALID_URL = r'''(?x)
https?://
(?:
(?:(?:embed|www)\.)?acast\.com/|
play\.acast\.com/s/
)
(?P<channel>[^/]+)/(?P<id>[^/#?]+)
'''
_TESTS = [{
'url': 'https://www.acast.com/sparpodcast/2.raggarmordet-rosterurdetforflutna',
'md5': 'f5598f3ad1e4776fed12ec1407153e4b',
'info_dict': {
'id': '2a92b283-1a75-4ad8-8396-499c641de0d9',
'ext': 'mp3',
'title': '2. Raggarmordet - Röster ur det förflutna',
'description': 'md5:a992ae67f4d98f1c0141598f7bebbf67',
'timestamp': 1477346700,
'upload_date': '20161024',
'duration': 2766,
'creator': 'Anton Berg & Martin Johnson',
'series': 'Spår',
'episode': '2. Raggarmordet - Röster ur det förflutna',
}
}, {
'url': 'http://embed.acast.com/adambuxton/ep.12-adam-joeschristmaspodcast2015',
'only_matching': True,
}, {
'url': 'https://play.acast.com/s/rattegangspodden/s04e09styckmordetihelenelund-del2-2',
'only_matching': True,
}, {
'url': 'https://play.acast.com/s/sparpodcast/2a92b283-1a75-4ad8-8396-499c641de0d9',
'only_matching': True,
}]
def _real_extract(self, url):
channel, display_id = re.match(self._VALID_URL, url).groups()
episode = self._call_api(
'%s/episodes/%s' % (channel, display_id),
display_id, {'showInfo': 'true'})
return self._extract_episode(
episode, self._extract_show_info(episode.get('show') or {}))
class ACastChannelIE(ACastBaseIE):
IE_NAME = 'acast:channel'
_VALID_URL = r'''(?x)
https?://
(?:
(?:www\.)?acast\.com/|
play\.acast\.com/s/
)
(?P<id>[^/#?]+)
'''
_TESTS = [{
'url': 'https://www.acast.com/todayinfocus',
'info_dict': {
'id': '4efc5294-5385-4847-98bd-519799ce5786',
'title': 'Today in Focus',
'description': 'md5:c09ce28c91002ce4ffce71d6504abaae',
},
'playlist_mincount': 200,
}, {
'url': 'http://play.acast.com/s/ft-banking-weekly',
'only_matching': True,
}]
@classmethod
def suitable(cls, url):
return False if ACastIE.suitable(url) else super(ACastChannelIE, cls).suitable(url)
def _real_extract(self, url):
show_slug = self._match_id(url)
show = self._call_api(show_slug, show_slug)
show_info = self._extract_show_info(show)
entries = []
for episode in (show.get('episodes') or []):
entries.append(self._extract_episode(episode, show_info))
return self.playlist_result(
entries, show.get('id'), show.get('title'), show.get('description'))
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
ytdl-org/youtube-dl | https://github.com/ytdl-org/youtube-dl/blob/956b8c585591b401a543e409accb163eeaaa1193/youtube_dl/extractor/izlesene.py | youtube_dl/extractor/izlesene.py | # coding: utf-8
from __future__ import unicode_literals
from .common import InfoExtractor
from ..compat import (
compat_str,
compat_urllib_parse_unquote,
)
from ..utils import (
determine_ext,
float_or_none,
get_element_by_id,
int_or_none,
parse_iso8601,
str_to_int,
)
class IzleseneIE(InfoExtractor):
_VALID_URL = r'''(?x)
https?://(?:(?:www|m)\.)?izlesene\.com/
(?:video|embedplayer)/(?:[^/]+/)?(?P<id>[0-9]+)
'''
_TESTS = [
{
'url': 'http://www.izlesene.com/video/sevincten-cildirtan-dogum-gunu-hediyesi/7599694',
'md5': '4384f9f0ea65086734b881085ee05ac2',
'info_dict': {
'id': '7599694',
'ext': 'mp4',
'title': 'Sevinçten Çıldırtan Doğum Günü Hediyesi',
'description': 'md5:253753e2655dde93f59f74b572454f6d',
'thumbnail': r're:^https?://.*\.jpg',
'uploader_id': 'pelikzzle',
'timestamp': int,
'upload_date': '20140702',
'duration': 95.395,
'age_limit': 0,
}
},
{
'url': 'http://www.izlesene.com/video/tarkan-dortmund-2006-konseri/17997',
'md5': '97f09b6872bffa284cb7fa4f6910cb72',
'info_dict': {
'id': '17997',
'ext': 'mp4',
'title': 'Tarkan Dortmund 2006 Konseri',
'thumbnail': r're:^https://.*\.jpg',
'uploader_id': 'parlayankiz',
'timestamp': int,
'upload_date': '20061112',
'duration': 253.666,
'age_limit': 0,
}
},
]
def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage('http://www.izlesene.com/video/%s' % video_id, video_id)
video = self._parse_json(
self._search_regex(
r'videoObj\s*=\s*({.+?})\s*;\s*\n', webpage, 'streams'),
video_id)
title = video.get('videoTitle') or self._og_search_title(webpage)
formats = []
for stream in video['media']['level']:
source_url = stream.get('source')
if not source_url or not isinstance(source_url, compat_str):
continue
ext = determine_ext(url, 'mp4')
quality = stream.get('value')
height = int_or_none(quality)
formats.append({
'format_id': '%sp' % quality if quality else 'sd',
'url': compat_urllib_parse_unquote(source_url),
'ext': ext,
'height': height,
})
self._sort_formats(formats)
description = self._og_search_description(webpage, default=None)
thumbnail = video.get('posterURL') or self._proto_relative_url(
self._og_search_thumbnail(webpage), scheme='http:')
uploader = self._html_search_regex(
r"adduserUsername\s*=\s*'([^']+)';",
webpage, 'uploader', fatal=False)
timestamp = parse_iso8601(self._html_search_meta(
'uploadDate', webpage, 'upload date'))
duration = float_or_none(video.get('duration') or self._html_search_regex(
r'videoduration["\']?\s*=\s*(["\'])(?P<value>(?:(?!\1).)+)\1',
webpage, 'duration', fatal=False, group='value'), scale=1000)
view_count = str_to_int(get_element_by_id('videoViewCount', webpage))
comment_count = self._html_search_regex(
r'comment_count\s*=\s*\'([^\']+)\';',
webpage, 'comment_count', fatal=False)
return {
'id': video_id,
'title': title,
'description': description,
'thumbnail': thumbnail,
'uploader_id': uploader,
'timestamp': timestamp,
'duration': duration,
'view_count': int_or_none(view_count),
'comment_count': int_or_none(comment_count),
'age_limit': self._family_friendly_search(webpage),
'formats': formats,
}
| python | Unlicense | 956b8c585591b401a543e409accb163eeaaa1193 | 2026-01-04T14:38:15.437342Z | false |
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