text stringlengths 31 243k | type stringclasses 1
value | start int64 36 275k | end int64 286 280k | depth int64 0 1 | filepath stringlengths 85 188 | parent_class stringclasses 3
values | class_index int64 0 10.8k |
|---|---|---|---|---|---|---|---|
class TFSeq2SeqLMOutput(ModelOutput):
"""
Base class for sequence-to-sequence language models outputs.
Args:
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `labels` is provided):
Language modeling loss.
logits (`tf.Tensor... | class_definition | 27,417 | 31,307 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 300 |
class TFNextSentencePredictorOutput(ModelOutput):
"""
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of non-masked labels, returned when `next_sentence_label` is provided):
N... | class_definition | 31,321 | 32,892 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 301 |
class TFSequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`... | class_definition | 32,906 | 34,390 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 302 |
class TFSeq2SeqSequenceClassifierOutput(ModelOutput):
"""
Base class for outputs of sequence-to-sequence sentence classification models.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `label` is provided):
Classification (or regression if config.num_labels==1) loss.
... | class_definition | 34,404 | 38,103 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 303 |
class TFSemanticSegmenterOutput(ModelOutput):
"""
Base class for outputs of semantic segmentation models.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`tf.Tensor` of... | class_definition | 38,117 | 40,004 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 304 |
class TFSemanticSegmenterOutputWithNoAttention(ModelOutput):
"""
Base class for outputs of semantic segmentation models that do not output attention scores.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.... | class_definition | 40,018 | 41,508 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 305 |
class TFImageClassifierOutput(ModelOutput):
"""
Base class for outputs of image classification models.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`tf.Tensor` of sh... | class_definition | 41,522 | 43,002 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 306 |
class TFMultipleChoiceModelOutput(ModelOutput):
"""
Base class for outputs of multiple choice models.
Args:
loss (`tf.Tensor` of shape *(batch_size, )*, *optional*, returned when `labels` is provided):
Classification loss.
logits (`tf.Tensor` of shape `(batch_size, num_choices)`... | class_definition | 43,016 | 44,505 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 307 |
class TFTokenClassifierOutput(ModelOutput):
"""
Base class for outputs of token classification models.
Args:
loss (`tf.Tensor` of shape `(n,)`, *optional*, where n is the number of unmasked labels, returned when `labels` is provided) :
Classification loss.
logits (`tf.Tensor` of... | class_definition | 44,519 | 45,967 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 308 |
class TFQuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of question answering models.
Args:
loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `start_positions` and `end_positions` are provided):
Total span extraction loss is the sum of a Cross-... | class_definition | 45,981 | 47,643 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 309 |
class TFSeq2SeqQuestionAnsweringModelOutput(ModelOutput):
"""
Base class for outputs of sequence-to-sequence question answering models.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for ... | class_definition | 47,657 | 51,168 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 310 |
class TFSequenceClassifierOutputWithPast(ModelOutput):
"""
Base class for outputs of sentence classification models.
Args:
loss (`tf.Tensor` of shape `(batch_size, )`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
l... | class_definition | 51,182 | 53,206 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 311 |
class TFImageClassifierOutputWithNoAttention(ModelOutput):
"""
Base class for outputs of image classification models.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`t... | class_definition | 53,220 | 54,274 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 312 |
class TFMaskedImageModelingOutput(ModelOutput):
"""
Base class for outputs of masked image completion / in-painting models.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
Reconstruction loss.
reconstruction (`tf.Tensor` of shap... | class_definition | 54,288 | 56,073 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_outputs.py | null | 313 |
class TensorFlowBenchmark(Benchmark):
args: TensorFlowBenchmarkArguments
configs: PretrainedConfig
framework: str = "TensorFlow"
@property
def framework_version(self):
return tf.__version__
def _inference_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float:
... | class_definition | 2,522 | 13,245 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_tf.py | null | 314 |
class PyTorchBenchmark(Benchmark):
args: PyTorchBenchmarkArguments
configs: PretrainedConfig
framework: str = "PyTorch"
@property
def framework_version(self):
return torch.__version__
def _inference_speed(self, model_name: str, batch_size: int, sequence_length: int) -> float:
_... | class_definition | 1,365 | 10,746 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark.py | null | 315 |
class PyTorchBenchmarkArguments(BenchmarkArguments):
deprecated_args = [
"no_inference",
"no_cuda",
"no_tpu",
"no_speed",
"no_memory",
"no_env_print",
"no_multi_process",
]
def __init__(self, **kwargs):
"""
This __init__ is there for l... | class_definition | 1,120 | 4,049 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args.py | null | 316 |
class BenchmarkArguments:
"""
BenchMarkArguments are arguments we use in our benchmark scripts **which relate to the training loop itself**.
Using `HfArgumentParser` we can turn this class into argparse arguments to be able to specify them on the command
line.
"""
models: List[str] = list_fiel... | class_definition | 1,002 | 6,498 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args_utils.py | null | 317 |
class Frame(NamedTuple):
"""
`Frame` is a NamedTuple used to gather the current frame state. `Frame` has the following fields:
- 'filename' (string): Name of the file currently executed
- 'module' (string): Name of the module currently executed
- 'line_number' (int): Number of the line ... | class_definition | 3,544 | 4,136 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py | null | 318 |
class UsedMemoryState(NamedTuple):
"""
`UsedMemoryState` are named tuples with the following fields:
- 'frame': a `Frame` namedtuple (see below) storing information on the current tracing frame (current file,
location in current file)
- 'cpu_memory': CPU RSS memory state *before* exec... | class_definition | 4,139 | 4,676 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py | null | 319 |
class Memory(NamedTuple):
"""
`Memory` NamedTuple have a single field `bytes` and you can get a human readable str of the number of mega bytes by
calling `__repr__`
- `byte` (integer): number of bytes,
"""
bytes: int
def __repr__(self) -> str:
return str(bytes_to_mega_bytes(se... | class_definition | 4,679 | 5,009 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py | null | 320 |
class MemoryState(NamedTuple):
"""
`MemoryState` are namedtuples listing frame + CPU/GPU memory with the following fields:
- `frame` (`Frame`): the current frame (see above)
- `cpu`: CPU memory consumed at during the current frame as a `Memory` named tuple
- `gpu`: GPU memory consumed a... | class_definition | 5,012 | 5,563 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py | null | 321 |
class MemorySummary(NamedTuple):
"""
`MemorySummary` namedtuple otherwise with the fields:
- `sequential`: a list of `MemoryState` namedtuple (see below) computed from the provided `memory_trace` by
subtracting the memory after executing each line from the memory before executing said line.
... | class_definition | 5,566 | 6,612 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py | null | 322 |
class MemoryMeasureProcess(Process):
"""
`MemoryMeasureProcess` inherits from `Process` and overwrites its `run()` method. Used to measure the
memory usage of a process
"""
def __init__(self, process_id: int, child_connection: Connection, interval: float):
... | class_definition | 8,480 | 9,622 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py | null | 323 |
class Benchmark(ABC):
"""
Benchmarks is a simple but feature-complete benchmarking script to compare memory and time performance of models in
Transformers.
"""
args: BenchmarkArguments
configs: PretrainedConfig
framework: str
def __init__(self, args: BenchmarkArguments = None, configs:... | class_definition | 22,684 | 37,598 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_utils.py | null | 324 |
class TensorFlowBenchmarkArguments(BenchmarkArguments):
deprecated_args = [
"no_inference",
"no_cuda",
"no_tpu",
"no_speed",
"no_memory",
"no_env_print",
"no_multi_process",
]
def __init__(self, **kwargs):
"""
This __init__ is there fo... | class_definition | 976 | 4,734 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/benchmark/benchmark_args_tf.py | null | 325 |
class SelectiveScanFn(torch.autograd.Function):
@staticmethod
def forward(
ctx, u, delta, A, B, C, D=None, z=None, delta_bias=None, delta_softplus=False, return_last_state=False
):
if u.stride(-1) != 1:
u = u.contiguous()
if delta.stride(-1) != 1:
delta = delt... | class_definition | 1,336 | 4,206 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py | null | 326 |
class MambaInnerFn(torch.autograd.Function):
@staticmethod
@custom_fwd
def forward(
ctx,
xz,
conv1d_weight,
conv1d_bias,
x_proj_weight,
delta_proj_weight,
out_proj_weight,
out_proj_bias,
A,
B=None,
C=None,
D=None... | class_definition | 7,500 | 18,893 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/kernels/falcon_mamba/selective_scan_with_ln_interface.py | null | 327 |
class PatchingSpec:
"""
Data class that holds patching specifications.
Args:
o: Module / object where the op to patch is located
name: Name of the op to monkey patch
custom_op: Custom op that patches the original op
orig_op: Original op that is being patched
op_wrapp... | class_definition | 1,543 | 2,149 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py | null | 328 |
class OnnxConfig(ABC):
"""
Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.
"""
default_fixed_batch = 2
default_fixed_sequence = 8
default_fixed_num_choices = 4
torch_onnx_minimum_version = version.parse("1.8")
_tasks_to_common... | class_definition | 2,152 | 18,850 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py | null | 329 |
class OnnxConfigWithPast(OnnxConfig, ABC):
def __init__(
self,
config: "PretrainedConfig",
task: str = "default",
patching_specs: List[PatchingSpec] = None,
use_past: bool = False,
):
super().__init__(config, task=task, patching_specs=patching_specs)
self.... | class_definition | 18,853 | 24,825 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py | null | 330 |
class OnnxSeq2SeqConfigWithPast(OnnxConfigWithPast):
@property
def outputs(self) -> Mapping[str, Mapping[int, str]]:
common_outputs = super(OnnxConfigWithPast, self).outputs
# Renaming the outputs axes properly.
for name, axes_names in common_outputs.items():
sequence_name = ... | class_definition | 24,828 | 32,555 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/config.py | null | 331 |
class FeaturesManager:
_TASKS_TO_AUTOMODELS = {}
_TASKS_TO_TF_AUTOMODELS = {}
if is_torch_available():
_TASKS_TO_AUTOMODELS = {
"default": AutoModel,
"masked-lm": AutoModelForMaskedLM,
"causal-lm": AutoModelForCausalLM,
"seq2seq-lm": AutoModelForSeq2Se... | class_definition | 2,811 | 28,263 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/features.py | null | 332 |
class ParameterFormat(Enum):
Float = c_float
@property
def size(self) -> int:
"""
Number of byte required for this data type
Returns:
Integer > 0
"""
return sizeof(self.value) | class_definition | 822 | 1,063 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/onnx/utils.py | null | 333 |
class ImageLoss(nn.Module):
"""
This class computes the losses for DetrForObjectDetection/DetrForSegmentation. The process happens in two steps: 1)
we compute hungarian assignment between ground truth boxes and the outputs of the model 2) we supervise each pair
of matched ground-truth / prediction (supe... | class_definition | 3,155 | 12,632 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py | null | 334 |
class HungarianMatcher(nn.Module):
"""
This class computes an assignment between the targets and the predictions of the network.
For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more
predictions than targets. In this case, we do a 1-to-1 matching o... | class_definition | 12,719 | 16,676 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py | null | 335 |
class NestedTensor:
def __init__(self, tensors, mask: Optional[Tensor]):
self.tensors = tensors
self.mask = mask
def to(self, device):
cast_tensor = self.tensors.to(device)
mask = self.mask
if mask is not None:
cast_mask = mask.to(device)
else:
... | class_definition | 19,545 | 20,062 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_for_object_detection.py | null | 336 |
class DeformableDetrHungarianMatcher(HungarianMatcher):
@torch.no_grad()
def forward(self, outputs, targets):
"""
Differences:
- out_prob = outputs["logits"].flatten(0, 1).sigmoid() instead of softmax
- class_cost uses alpha and gamma
"""
batch_size, num_queries =... | class_definition | 361 | 2,240 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_deformable_detr.py | null | 337 |
class DeformableDetrImageLoss(ImageLoss):
def __init__(self, matcher, num_classes, focal_alpha, losses):
nn.Module.__init__(self)
self.matcher = matcher
self.num_classes = num_classes
self.focal_alpha = focal_alpha
self.losses = losses
# removed logging parameter, which ... | class_definition | 2,243 | 3,988 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_deformable_detr.py | null | 338 |
class RTDetrHungarianMatcher(nn.Module):
"""This class computes an assignment between the targets and the predictions of the network
For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more
predictions than targets. In this case, we do a 1-to-1 matching o... | class_definition | 1,110 | 5,300 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py | null | 339 |
class RTDetrLoss(nn.Module):
"""
This class computes the losses for RTDetr. The process happens in two steps: 1) we compute hungarian assignment
between ground truth boxes and the outputs of the model 2) we supervise each pair of matched ground-truth /
prediction (supervise class and box).
Args:
... | class_definition | 5,303 | 20,263 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/loss/loss_rt_detr.py | null | 340 |
class CompressedTensorsHfQuantizer(HfQuantizer):
"""
Quantizer for the compressed_tensors package. Loads and restores models to
quantized state with compressed_tensors
"""
requires_calibration = True
required_packages = ["compressed_tensors"]
def __init__(self, quantization_config: Compre... | class_definition | 884 | 5,366 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_compressed_tensors.py | null | 341 |
class FbgemmFp8HfQuantizer(HfQuantizer):
"""
FP8 quantization using fbgemm kernels
"""
requires_parameters_quantization = True
requires_calibration = False
required_packages = ["fbgemm-gpu", "accelerate"]
def __init__(self, quantization_config, **kwargs):
super().__init__(quantiza... | class_definition | 1,055 | 8,141 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_fbgemm_fp8.py | null | 342 |
class AwqQuantizer(HfQuantizer):
"""
4-bit quantization for Activation-aware Weight Quantization(AWQ) (https://arxiv.org/abs/2306.00978)
"""
# AWQ requires data callibration - we support only inference
requires_calibration = True
required_packages = ["awq", "accelerate"]
def __init__(self... | class_definition | 1,040 | 6,901 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_awq.py | null | 343 |
class Bnb4BitHfQuantizer(HfQuantizer):
"""
4-bit quantization from bitsandbytes.py quantization method:
before loading: converts transformer layers into Linear4bit during loading: load 16bit weight and pass to the
layer object after: quantizes individual weights in Linear4bit into 4bit at the fi... | class_definition | 1,246 | 16,330 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_4bit.py | null | 344 |
class TorchAoHfQuantizer(HfQuantizer):
"""
Quantizer for torchao: https://github.com/pytorch/ao/
"""
requires_parameters_quantization = True
requires_calibration = False
required_packages = ["torchao"]
def __init__(self, quantization_config, **kwargs):
super().__init__(quantization... | class_definition | 2,144 | 10,119 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_torchao.py | null | 345 |
class GptqHfQuantizer(HfQuantizer):
"""
Quantizer of the GPTQ method - for GPTQ the quantizer support calibration of the model through
`auto_gptq` or `gptqmodel` package. Quantization is done under the hood for users if they load a non-prequantized model.
"""
requires_calibration = False
requir... | class_definition | 1,082 | 5,430 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_gptq.py | null | 346 |
class QuantoHfQuantizer(HfQuantizer):
"""
Quantizer for the quanto library
"""
required_packages = ["quanto", "accelerate"]
requires_parameters_quantization = True
requires_calibration = False
def __init__(self, quantization_config: QuantoConfig, **kwargs):
super().__init__(quantiz... | class_definition | 1,139 | 8,106 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_quanto.py | null | 347 |
class EetqHfQuantizer(HfQuantizer):
"""
8-bit quantization from EETQ quantization method:
before loading: converts transformer layers into W8A16Linear during loading: load 16bit weight and pass to the
layer object after: quantizes individual weights in Linear8bitLt into 8bit at first .cuda() cal... | class_definition | 1,001 | 7,325 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_eetq.py | null | 348 |
class BitNetHfQuantizer(HfQuantizer):
"""
1.58-bit quantization from BitNet quantization method:
Before loading: it converts the linear layers into BitLinear layers during loading.
Checkout the paper introducing this method : https://arxiv.org/pdf/2402.17764
"""
requires_parameters_quantizatio... | class_definition | 923 | 4,301 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bitnet.py | null | 349 |
class AqlmHfQuantizer(HfQuantizer):
"""
Quantizer of the AQLM method. Enables the loading of prequantized models.
"""
requires_calibration = True
required_packages = ["aqlm"]
optimum_quantizer = None
def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
super(... | class_definition | 1,096 | 3,691 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_aqlm.py | null | 350 |
class HqqHfQuantizer(HfQuantizer):
"""
HQQ quantizer base HF class.
nn.Linear modules are first tagged with quant_config in _process_model_before_weight_loading().
The actual quantization and offloading to the GPU is done in check_quantized_param().
"""
use_keep_in_fp32_modules = False
requ... | class_definition | 1,341 | 11,437 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_hqq.py | null | 351 |
class VptqHfQuantizer(HfQuantizer):
"""
Quantizer of the VPTQ method. Enables the loading of prequantized models.
"""
requires_calibration = True
required_packages = ["vptq"]
def __init__(self, quantization_config: QuantizationConfigMixin, **kwargs):
super().__init__(quantization_confi... | class_definition | 996 | 3,719 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_vptq.py | null | 352 |
class Bnb8BitHfQuantizer(HfQuantizer):
"""
8-bit quantization from bitsandbytes quantization method:
before loading: converts transformer layers into Linear8bitLt during loading: load 16bit weight and pass to the
layer object after: quantizes individual weights in Linear8bitLt into 8bit at fitst... | class_definition | 1,180 | 13,893 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_bnb_8bit.py | null | 353 |
class HfQuantizer(ABC):
"""
Abstract class of the HuggingFace quantizer. Supports for now quantizing HF transformers models for inference and/or quantization.
This class is used only for transformers.PreTrainedModel.from_pretrained and cannot be easily used outside the scope of that method
yet.
Att... | class_definition | 932 | 10,501 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/base.py | null | 354 |
class HiggsHfQuantizer(HfQuantizer):
"""
Quantizer of the HIGGS method. Enables the loading of prequantized models and in-flight quantization of full-precision models.
"""
requires_calibration = False
requires_parameters_quantization = True
required_packages = ["flute-kernel", "fast_hadamard_tr... | class_definition | 1,583 | 9,576 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/quantizer_higgs.py | null | 355 |
class AutoQuantizationConfig:
"""
The Auto-HF quantization config class that takes care of automatically dispatching to the correct
quantization config given a quantization config stored in a dictionary.
"""
@classmethod
def from_dict(cls, quantization_config_dict: Dict):
quant_method =... | class_definition | 2,727 | 4,829 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/auto.py | null | 356 |
class AutoHfQuantizer:
"""
The Auto-HF quantizer class that takes care of automatically instantiating to the correct
`HfQuantizer` given the `QuantizationConfig`.
"""
@classmethod
def from_config(cls, quantization_config: Union[QuantizationConfigMixin, Dict], **kwargs):
# Convert it to... | class_definition | 4,832 | 8,014 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/quantizers/auto.py | null | 357 |
class ImageQuestionAnsweringTool(PipelineTool):
default_checkpoint = "dandelin/vilt-b32-finetuned-vqa"
description = (
"This is a tool that answers a question about an image. It "
"returns a text that is the answer to the question."
)
name = "image_qa"
pre_processor_class = AutoProce... | class_definition | 835 | 2,003 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/image_question_answering.py | null | 358 |
class Problem:
"""
A class regrouping all the information to solve a problem on which we will evaluate agents.
Args:
task (`str` ou `list[str]`):
One or several descriptions of the task to perform. If a list, it should contain variations on the
phrasing, but for the same tas... | class_definition | 2,931 | 3,889 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/evaluate_agent.py | null | 359 |
class ChatMessage:
def __init__(self, role, content, metadata=None):
self.role = role
self.content = content
self.metadata = metadata | class_definition | 943 | 1,152 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/monitoring.py | null | 360 |
class ChatMessage:
def __init__(self, role, content, metadata=None):
self.role = role
self.content = content
self.metadata = metadata | class_definition | 2,409 | 2,618 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/monitoring.py | null | 361 |
class Monitor:
def __init__(self, tracked_llm_engine):
self.step_durations = []
self.tracked_llm_engine = tracked_llm_engine
if getattr(self.tracked_llm_engine, "last_input_token_count", "Not found") != "Not found":
self.total_input_token_count = 0
self.total_output_t... | class_definition | 3,628 | 4,683 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/monitoring.py | null | 362 |
class Tool:
"""
A base class for the functions used by the agent. Subclass this and implement the `__call__` method as well as the
following class attributes:
- **description** (`str`) -- A short description of what your tool does, the inputs it expects and the output(s) it
will return. For insta... | class_definition | 3,043 | 23,689 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py | null | 363 |
class SpaceToolWrapper(Tool):
def __init__(
self,
space_id: str,
name: str,
description: str,
api_name: Optional[str] = None,
token: Optional[str] = None,
):
self.client = Client(space... | class_definition | 18,293 | 21,668 | 1 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py | Tool | 364 |
class GradioToolWrapper(Tool):
def __init__(self, _gradio_tool):
self.name = _gradio_tool.name
self.description = _gradio_tool.description
self.output_type = "string"
self._gradio_tool = _gradio_tool
func_args = list(inspect.sig... | class_definition | 21,919 | 22,502 | 1 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py | Tool | 365 |
class LangChainToolWrapper(Tool):
def __init__(self, _langchain_tool):
self.name = _langchain_tool.name.lower()
self.description = _langchain_tool.description
self.inputs = _langchain_tool.args.copy()
for input_content in self.inputs.values():
... | class_definition | 22,692 | 23,636 | 1 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py | Tool | 366 |
class PipelineTool(Tool):
"""
A [`Tool`] tailored towards Transformer models. On top of the class attributes of the base class [`Tool`], you will
need to specify:
- **model_class** (`type`) -- The class to use to load the model in this tool.
- **default_checkpoint** (`str`) -- The default checkpoin... | class_definition | 24,882 | 31,025 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py | null | 367 |
class EndpointClient:
def __init__(self, endpoint_url: str, token: Optional[str] = None):
self.headers = {
**build_hf_headers(token=token),
"Content-Type": "application/json",
}
self.endpoint_url = endpoint_url
@staticmethod
def encode_image(image):
_... | class_definition | 35,377 | 36,954 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py | null | 368 |
class ToolCollection:
"""
Tool collections enable loading all Spaces from a collection in order to be added to the agent's toolbox.
> [!NOTE]
> Only Spaces will be fetched, so you can feel free to add models and datasets to your collection if you'd
> like for this collection to showcase them.
... | class_definition | 36,957 | 38,198 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py | null | 369 |
class SpecificTool(Tool):
name = parameters["name"]
description = parameters["description"]
inputs = parameters["parameters"]["properties"]
output_type = parameters["return"]["type"]
@wraps(tool_function)
def forward(self, *args, **kwargs):
return tool_functi... | class_definition | 38,825 | 39,164 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/tools.py | null | 370 |
class MessageRole(str, Enum):
USER = "user"
ASSISTANT = "assistant"
SYSTEM = "system"
TOOL_CALL = "tool-call"
TOOL_RESPONSE = "tool-response"
@classmethod
def roles(cls):
return [r.value for r in cls] | class_definition | 920 | 1,157 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py | null | 371 |
class HfEngine:
def __init__(self, model_id: Optional[str] = None):
self.last_input_token_count = None
self.last_output_token_count = None
if model_id is None:
model_id = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
logger.warning(f"Using default model for token counting: '{... | class_definition | 2,356 | 5,573 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py | null | 372 |
class HfApiEngine(HfEngine):
"""A class to interact with Hugging Face's Inference API for language model interaction.
This engine allows you to communicate with Hugging Face's models using the Inference API. It can be used in both serverless mode or with a dedicated endpoint, supporting features like stop sequ... | class_definition | 5,576 | 7,837 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py | null | 373 |
class TransformersEngine(HfEngine):
"""This engine uses a pre-initialized local text-generation pipeline."""
def __init__(self, pipeline: Pipeline, model_id: Optional[str] = None):
super().__init__(model_id)
self.pipeline = pipeline
def generate(
self,
messages: List[Dict[s... | class_definition | 7,840 | 8,887 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/llm_engine.py | null | 374 |
class SpeechToTextTool(PipelineTool):
default_checkpoint = "distil-whisper/distil-large-v3"
description = "This is a tool that transcribes an audio into text. It returns the transcribed text."
name = "transcriber"
pre_processor_class = WhisperProcessor
model_class = WhisperForConditionalGeneration
... | class_definition | 763 | 1,495 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/speech_to_text.py | null | 375 |
class CustomFormatter(logging.Formatter):
grey = "\x1b[38;20m"
bold_yellow = "\x1b[33;1m"
red = "\x1b[31;20m"
green = "\x1b[32;20m"
bold_green = "\x1b[32;20;1m"
bold_red = "\x1b[31;1m"
bold_white = "\x1b[37;1m"
orange = "\x1b[38;5;214m"
bold_orange = "\x1b[38;5;214;1m"
reset = "\... | class_definition | 1,827 | 2,835 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py | null | 376 |
class Toolbox:
"""
The toolbox contains all tools that the agent can perform operations with, as well as a few methods to
manage them.
Args:
tools (`List[Tool]`):
The list of tools to instantiate the toolbox with
add_base_tools (`bool`, defaults to `False`, *optional*, defau... | class_definition | 6,369 | 10,031 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py | null | 377 |
class AgentError(Exception):
"""Base class for other agent-related exceptions"""
def __init__(self, message):
super().__init__(message)
self.message = message | class_definition | 10,034 | 10,217 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py | null | 378 |
class AgentParsingError(AgentError):
"""Exception raised for errors in parsing in the agent"""
pass | class_definition | 10,220 | 10,328 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py | null | 379 |
class AgentExecutionError(AgentError):
"""Exception raised for errors in execution in the agent"""
pass | class_definition | 10,331 | 10,443 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py | null | 380 |
class AgentMaxIterationsError(AgentError):
"""Exception raised for errors in execution in the agent"""
pass | class_definition | 10,446 | 10,562 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py | null | 381 |
class AgentGenerationError(AgentError):
"""Exception raised for errors in generation in the agent"""
pass | class_definition | 10,565 | 10,679 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py | null | 382 |
class Agent:
def __init__(
self,
tools: Union[List[Tool], Toolbox],
llm_engine: Callable = None,
system_prompt: Optional[str] = None,
tool_description_template: Optional[str] = None,
additional_args: Dict = {},
max_iterations: int = 6,
tool_parser: Opt... | class_definition | 12,433 | 23,079 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py | null | 383 |
class CodeAgent(Agent):
"""
A class for an agent that solves the given task using a single block of code. It plans all its actions, then executes all in one shot.
"""
def __init__(
self,
tools: List[Tool],
llm_engine: Optional[Callable] = None,
system_prompt: Optional[st... | class_definition | 23,082 | 27,909 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py | null | 384 |
class ReactAgent(Agent):
"""
This agent that solves the given task step by step, using the ReAct framework:
While the objective is not reached, the agent will perform a cycle of thinking and acting.
The action will be parsed from the LLM output: it consists in calls to tools from the toolbox, with argum... | class_definition | 27,912 | 39,268 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py | null | 385 |
class ReactJsonAgent(ReactAgent):
"""
This agent that solves the given task step by step, using the ReAct framework:
While the objective is not reached, the agent will perform a cycle of thinking and acting.
The tool calls will be formulated by the LLM in JSON format, then parsed and executed.
"""
... | class_definition | 39,271 | 43,831 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py | null | 386 |
class ReactCodeAgent(ReactAgent):
"""
This agent that solves the given task step by step, using the ReAct framework:
While the objective is not reached, the agent will perform a cycle of thinking and acting.
The tool calls will be formulated by the LLM in code format, then parsed and executed.
"""
... | class_definition | 43,834 | 49,471 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py | null | 387 |
class ManagedAgent:
def __init__(self, agent, name, description, additional_prompting=None, provide_run_summary=False):
self.agent = agent
self.name = name
self.description = description
self.additional_prompting = additional_prompting
self.provide_run_summary = provide_run_s... | class_definition | 49,507 | 52,065 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/agents.py | null | 388 |
class TextToSpeechTool(PipelineTool):
default_checkpoint = "microsoft/speecht5_tts"
description = (
"This is a tool that reads an English text out loud. It returns a waveform object containing the sound."
)
name = "text_to_speech"
pre_processor_class = SpeechT5Processor
model_class = Spe... | class_definition | 898 | 2,467 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/text_to_speech.py | null | 389 |
class DocumentQuestionAnsweringTool(PipelineTool):
default_checkpoint = "naver-clova-ix/donut-base-finetuned-docvqa"
description = "This is a tool that answers a question about an document (pdf). It returns a string that contains the answer to the question."
name = "document_qa"
pre_processor_class = Au... | class_definition | 931 | 3,633 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/document_question_answering.py | null | 390 |
class TranslationTool(PipelineTool):
"""
Example:
```py
from transformers.agents import TranslationTool
translator = TranslationTool()
translator("This is a super nice API!", src_lang="English", tgt_lang="French")
```
"""
lang_to_code = LANGUAGE_CODES
default_checkpoint = "fac... | class_definition | 6,764 | 8,670 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/translation.py | null | 391 |
class PreTool:
name: str
inputs: Dict[str, str]
output_type: type
task: str
description: str
repo_id: str | class_definition | 2,141 | 2,270 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/default_tools.py | null | 392 |
class PythonInterpreterTool(Tool):
name = "python_interpreter"
description = "This is a tool that evaluates python code. It can be used to perform calculations."
output_type = "string"
def __init__(self, *args, authorized_imports=None, **kwargs):
if authorized_imports is None:
self... | class_definition | 3,828 | 4,959 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/default_tools.py | null | 393 |
class FinalAnswerTool(Tool):
name = "final_answer"
description = "Provides a final answer to the given problem."
inputs = {"answer": {"type": "any", "description": "The final answer to the problem"}}
output_type = "any"
def forward(self, answer):
return answer | class_definition | 4,962 | 5,251 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/default_tools.py | null | 394 |
class DuckDuckGoSearchTool(Tool):
name = "web_search"
description = """Perform a web search based on your query (think a Google search) then returns the top search results as a list of dict elements.
Each result has keys 'title', 'href' and 'body'."""
inputs = {"query": {"type": "string", "description":... | class_definition | 752 | 1,511 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/search.py | null | 395 |
class VisitWebpageTool(Tool):
name = "visit_webpage"
description = "Visits a webpage at the given url and returns its content as a markdown string."
inputs = {
"url": {
"type": "string",
"description": "The url of the webpage to visit.",
}
}
output_type = "str... | class_definition | 1,514 | 2,776 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/search.py | null | 396 |
class InterpreterError(ValueError):
"""
An error raised when the interpretor cannot evaluate a Python expression, due to syntax error or unsupported
operations.
"""
pass | class_definition | 933 | 1,123 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/python_interpreter.py | null | 397 |
class BreakException(Exception):
pass | class_definition | 1,571 | 1,612 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/python_interpreter.py | null | 398 |
class ContinueException(Exception):
pass | class_definition | 1,615 | 1,659 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/agents/python_interpreter.py | null | 399 |
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