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
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|---|---|---|---|---|---|---|---|
class Speech2TextProcessor(ProcessorMixin):
r"""
Constructs a Speech2Text processor which wraps a Speech2Text feature extractor and a Speech2Text tokenizer into a
single processor.
[`Speech2TextProcessor`] offers all the functionalities of [`Speech2TextFeatureExtractor`] and
[`Speech2TextTokenizer`... | class_definition | 757 | 4,818 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/speech_to_text/processing_speech_to_text.py | null | 3,300 |
class GemmaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar c... | class_definition | 1,504 | 7,757 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py | null | 3,301 |
class GemmaTokenizer(LlamaTokenizer, PreTrainedTokenizer):
"""
Construct a Gemma tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
no padding token in the original model.
Args:
vocab_file (`str`):
Path to the vocabulary file.
u... | class_definition | 7,760 | 14,786 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py | null | 3,302 |
class GemmaRMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.zeros(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
... | class_definition | 14,789 | 15,460 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py | null | 3,303 |
class GemmaMLP(LlamaMLP):
def __init__(self, config):
super().__init__()
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, ... | class_definition | 15,463 | 15,812 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py | null | 3,304 |
class GemmaModel(LlamaModel):
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds:... | class_definition | 15,815 | 20,493 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py | null | 3,305 |
class GemmaForCausalLM(LlamaForCausalLM):
def forward(**super_kwargs):
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
... | class_definition | 20,496 | 22,145 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py | null | 3,306 |
class GemmaForSequenceClassification(LlamaForSequenceClassification):
pass | class_definition | 22,148 | 22,226 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py | null | 3,307 |
class GemmaForTokenClassification(LlamaForTokenClassification):
pass | class_definition | 22,229 | 22,301 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modular_gemma.py | null | 3,308 |
class GemmaTokenizer(PreTrainedTokenizer):
"""
Construct a Gemma tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
no padding token in the original model.
Args:
vocab_file (`str`):
Path to the vocabulary file.
unk_token (`str` ... | class_definition | 1,875 | 14,134 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma.py | null | 3,309 |
class GemmaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GemmaModel`]. It is used to instantiate an Gemma
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar c... | class_definition | 1,505 | 7,758 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/configuration_gemma.py | null | 3,310 |
class GemmaTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a Gemma tokenizer fast. Based on byte-level Byte-Pair-Encoding.
This uses notably ByteFallback and no prefix space. Normalization is applied to replace `" "` with `"▁"`
```python
>>> from transformers import GemmaTokenizerFast
... | class_definition | 1,168 | 8,278 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/tokenization_gemma_fast.py | null | 3,311 |
class FlaxGemmaRMSNorm(nn.Module):
config: GemmaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.epsilon = self.config.rms_norm_eps
self.weight = self.param("weight", lambda _, shape: jnp.ones(shape), self.config.hidden_size)
def __call__(self, hidden_states):
varian... | class_definition | 7,245 | 7,943 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py | null | 3,312 |
class FlaxGemmaRotaryEmbedding(nn.Module):
config: GemmaConfig
dtype: jnp.dtype = jnp.float32
# Ignore copy
def setup(self):
head_dim = self.config.head_dim
self.sincos = create_sinusoidal_positions(self.config.max_position_embeddings, head_dim)
def __call__(self, key, query, posit... | class_definition | 8,049 | 8,729 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py | null | 3,313 |
class FlaxGemmaAttention(nn.Module):
config: GemmaConfig
dtype: jnp.dtype = jnp.float32
causal: bool = True
is_cross_attention: bool = False
def setup(self):
config = self.config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.hea... | class_definition | 8,732 | 15,846 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py | null | 3,314 |
class FlaxGemmaMLP(nn.Module):
config: GemmaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
embed_dim = self.config.hidden_size
inner_dim = self.config.intermediate_size if self.config.intermediate_size is not None else 4 * embed_dim
kernel_init = jax.nn.initializers.normal(... | class_definition | 15,849 | 17,499 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py | null | 3,315 |
class FlaxGemmaDecoderLayer(nn.Module):
config: GemmaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.input_layernorm = FlaxGemmaRMSNorm(self.config, dtype=self.dtype)
self.self_attn = FlaxGemmaAttention(self.config, dtype=self.dtype)
self.post_attention_layernorm = FlaxG... | class_definition | 17,602 | 19,015 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py | null | 3,316 |
class FlaxGemmaPreTrainedModel(FlaxPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = GemmaConfig
base_model_prefix = "model"
module_class: nn.Module = None
def __init__(
... | class_definition | 19,163 | 24,476 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py | null | 3,317 |
class FlaxGemmaLayerCollection(nn.Module):
config: GemmaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.blocks = [
FlaxGemmaDecoderLayer(self.config, dtype=self.dtype, name=str(i))
for i in range(self.config.num_hidden_layers)
]
def __call__(
... | class_definition | 24,582 | 26,057 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py | null | 3,318 |
class FlaxGemmaModule(nn.Module):
config: GemmaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.hidden_size = self.config.hidden_size
embedding_init = jax.nn.initializers.normal(stddev=self.config.initializer_range)
self.embed_tokens = nn.Embed(
self.config.vo... | class_definition | 26,154 | 28,134 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py | null | 3,319 |
class FlaxGemmaModel(FlaxGemmaPreTrainedModel):
module_class = FlaxGemmaModule | class_definition | 28,385 | 28,467 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py | null | 3,320 |
class FlaxGemmaForCausalLMModule(nn.Module):
config: GemmaConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.model = FlaxGemmaModule(self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.config.vocab_size,
use_bias=False,
dtype=self.dtype,... | class_definition | 28,746 | 30,379 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py | null | 3,321 |
class FlaxGemmaForCausalLM(FlaxGemmaPreTrainedModel):
module_class = FlaxGemmaForCausalLMModule
def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
# initializing the cache
batch_size, seq_length = input_ids.shape
past_key_values ... | class_definition | 30,631 | 32,156 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_flax_gemma.py | null | 3,322 |
class GemmaRMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.zeros(dim))
def _norm(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
... | class_definition | 2,479 | 3,150 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py | null | 3,323 |
class GemmaMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self... | class_definition | 3,153 | 3,821 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py | null | 3,324 |
class GemmaRotaryEmbedding(nn.Module):
def __init__(self, config: GemmaConfig, device=None):
super().__init__()
# BC: "rope_type" was originally "type"
if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
self.rope_type = config.rope_scaling.get("rope_type", co... | class_definition | 3,824 | 7,019 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py | null | 3,325 |
class GemmaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: GemmaConfig, layer_idx: int):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "head_dim", config.hid... | class_definition | 10,298 | 13,865 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py | null | 3,326 |
class GemmaDecoderLayer(nn.Module):
def __init__(self, config: GemmaConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = GemmaAttention(config=config, layer_idx=layer_idx)
self.mlp = GemmaMLP(config)
self.input_layernorm = Gemma... | class_definition | 13,868 | 15,938 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py | null | 3,327 |
class GemmaPreTrainedModel(PreTrainedModel):
config_class = GemmaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["GemmaDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_supp... | class_definition | 16,960 | 17,883 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py | null | 3,328 |
class GemmaModel(GemmaPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`GemmaDecoderLayer`]
Args:
config: GemmaConfig
"""
def __init__(self, config: GemmaConfig):
super().__init__(config)
self.padding_idx = config.p... | class_definition | 22,687 | 34,193 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py | null | 3,329 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 34,196 | 34,258 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py | null | 3,330 |
class GemmaForCausalLM(GemmaPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = GemmaModel(config)
self.vocab_size = config.vocab_size
self.lm_head ... | class_definition | 34,261 | 39,305 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py | null | 3,331 |
class GemmaForSequenceClassification(GemmaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = GemmaModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initialize weights a... | class_definition | 40,098 | 43,910 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py | null | 3,332 |
class GemmaForTokenClassification(GemmaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = GemmaModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = config.classi... | class_definition | 44,157 | 47,369 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/gemma/modeling_gemma.py | null | 3,333 |
class TimeSeriesTransformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`TimeSeriesTransformerModel`]. It is used to
instantiate a Time Series Transformer model according to the specified arguments, defining the model architecture.
Instantiating a confi... | class_definition | 842 | 11,656 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/configuration_time_series_transformer.py | null | 3,334 |
class TimeSeriesFeatureEmbedder(nn.Module):
"""
Embed a sequence of categorical features.
Args:
cardinalities (`list[int]`):
List of cardinalities of the categorical features.
embedding_dims (`list[int]`):
List of embedding dimensions of the categorical features.
... | class_definition | 1,616 | 2,799 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py | null | 3,335 |
class TimeSeriesStdScaler(nn.Module):
"""
Standardize features by calculating the mean and scaling along the first dimension, and then normalizes it by
subtracting from the mean and dividing by the standard deviation.
"""
def __init__(self, config: TimeSeriesTransformerConfig):
super().__in... | class_definition | 2,802 | 4,555 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py | null | 3,336 |
class TimeSeriesMeanScaler(nn.Module):
"""
Computes a scaling factor as the weighted average absolute value along the first dimension, and scales the data
accordingly.
"""
def __init__(self, config: TimeSeriesTransformerConfig):
super().__init__()
self.dim = config.scaling_dim if ha... | class_definition | 4,558 | 6,972 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py | null | 3,337 |
class TimeSeriesNOPScaler(nn.Module):
"""
Assigns a scaling factor equal to 1 along the first dimension, and therefore applies no scaling to the input data.
"""
def __init__(self, config: TimeSeriesTransformerConfig):
super().__init__()
self.dim = config.scaling_dim if hasattr(config, "... | class_definition | 6,975 | 8,189 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py | null | 3,338 |
class TimeSeriesSinusoidalPositionalEmbedding(nn.Embedding):
"""This module produces sinusoidal positional embeddings of any length."""
def __init__(self, num_positions: int, embedding_dim: int, padding_idx: Optional[int] = None) -> None:
super().__init__(num_positions, embedding_dim)
self.weig... | class_definition | 9,664 | 11,235 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py | null | 3,339 |
class TimeSeriesValueEmbedding(nn.Module):
def __init__(self, feature_size, d_model):
super().__init__()
self.value_projection = nn.Linear(in_features=feature_size, out_features=d_model, bias=False)
def forward(self, x):
return self.value_projection(x) | class_definition | 11,238 | 11,523 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py | null | 3,340 |
class TimeSeriesTransformerAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = F... | class_definition | 11,626 | 19,050 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py | null | 3,341 |
class TimeSeriesTransformerEncoderLayer(nn.Module):
def __init__(self, config: TimeSeriesTransformerConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = TIME_SERIES_TRANSFORMER_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
... | class_definition | 19,187 | 22,438 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py | null | 3,342 |
class TimeSeriesTransformerDecoderLayer(nn.Module):
def __init__(self, config: TimeSeriesTransformerConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = TIME_SERIES_TRANSFORMER_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
... | class_definition | 22,740 | 28,751 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py | null | 3,343 |
class TimeSeriesTransformerPreTrainedModel(PreTrainedModel):
config_class = TimeSeriesTransformerConfig
base_model_prefix = "model"
main_input_name = "past_values"
supports_gradient_checkpointing = True
def _init_weights(self, module):
std = self.config.init_std
if isinstance(module... | class_definition | 28,754 | 29,532 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py | null | 3,344 |
class TimeSeriesTransformerEncoder(TimeSeriesTransformerPreTrainedModel):
"""
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a
[`TimeSeriesTransformerEncoderLayer`].
Args:
config: TimeSeriesTransformerConfig
"""
def __init__(self, config:... | class_definition | 41,586 | 48,093 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py | null | 3,345 |
class TimeSeriesTransformerDecoder(TimeSeriesTransformerPreTrainedModel):
"""
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a
[`TimeSeriesTransformerDecoderLayer`]
Args:
config: TimeSeriesTransformerConfig
"""
def __init__(self, config: TimeSeriesTrans... | class_definition | 48,096 | 59,419 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py | null | 3,346 |
class TimeSeriesTransformerModel(TimeSeriesTransformerPreTrainedModel):
def __init__(self, config: TimeSeriesTransformerConfig):
super().__init__(config)
if config.scaling == "mean" or config.scaling is True:
self.scaler = TimeSeriesMeanScaler(config)
elif config.scaling == "std... | class_definition | 59,601 | 70,803 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py | null | 3,347 |
class TimeSeriesTransformerForPrediction(TimeSeriesTransformerPreTrainedModel):
def __init__(self, config: TimeSeriesTransformerConfig):
super().__init__(config)
self.model = TimeSeriesTransformerModel(config)
if config.distribution_output == "student_t":
self.distribution_output... | class_definition | 70,978 | 88,552 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/time_series_transformer/modeling_time_series_transformer.py | null | 3,348 |
class MllamaConverter(TikTokenConverter):
def __init__(
self,
vocab_file,
special_tokens: List[str],
pattern: str,
model_max_length: int,
chat_template: Optional[str] = None,
**kwargs,
):
super().__init__(vocab_file, pattern=pattern)
self.a... | class_definition | 24,227 | 24,947 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/convert_mllama_weights_to_hf.py | null | 3,349 |
class MllamaImageProcessor(BaseImageProcessor):
"""
Constructs a Mllama image processor.
Args:
do_convert_rgb (`bool`, *optional*, defaults to `True`):
Whether to convert the image to RGB. This is useful if the input image is of a different format e.g. RGBA.
Only has an effe... | class_definition | 21,800 | 39,175 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/image_processing_mllama.py | null | 3,350 |
class MllamaImagesKwargs(ImagesKwargs, total=False):
max_image_tiles: Optional[int] | class_definition | 1,095 | 1,182 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/processing_mllama.py | null | 3,351 |
class MllamaProcessorKwargs(ProcessingKwargs, total=False):
images_kwargs: MllamaImagesKwargs
_defaults = {
"image_kwargs": {
"max_image_tiles": 4,
},
} | class_definition | 1,185 | 1,378 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/processing_mllama.py | null | 3,352 |
class MllamaProcessor(ProcessorMixin):
r"""
Constructs a Mllama processor which wraps [`MllamaImageProcessor`] and
[`PretrainedTokenizerFast`] into a single processor that inherits both the image processor and
tokenizer functionalities. See the [`~MllamaProcessor.__call__`] and [`~OwlViTProcessor.decode... | class_definition | 6,914 | 16,276 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/processing_mllama.py | null | 3,353 |
class MllamaVisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MllamaVisionModel`]. It is used to instantiate an
Mllama vision model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults ... | class_definition | 876 | 6,517 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/configuration_mllama.py | null | 3,354 |
class MllamaTextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MllamaTextModel`]. It is used to instantiate an
Mllama text model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will y... | class_definition | 6,520 | 15,208 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/configuration_mllama.py | null | 3,355 |
class MllamaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`MllamaForConditionalGeneration`]. It is used to instantiate an
Mllama model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults ... | class_definition | 15,211 | 18,116 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/configuration_mllama.py | null | 3,356 |
class MllamaPrecomputedAspectRatioEmbedding(nn.Module):
def __init__(self, config: MllamaVisionConfig, is_gated: bool = True):
super().__init__()
self.max_num_tiles = config.max_num_tiles
self.hidden_size = config.hidden_size
self.max_aspect_ratio_id = config.max_aspect_ratio_id
... | class_definition | 3,758 | 4,687 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,357 |
class MllamaPrecomputedPositionEmbedding(nn.Module):
def __init__(self, config: MllamaVisionConfig):
super().__init__()
self.max_num_tiles = config.max_num_tiles
self.max_aspect_ratio_id = config.max_aspect_ratio_id
self.num_patches = (config.image_size // config.patch_size) ** 2 + 1... | class_definition | 4,690 | 6,332 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,358 |
class MllamaVisionMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.activation_fn = ACT2FN[config.hidden_act]
self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
self.fc2 = nn.Linear(config.intermediate_size, config.hidden... | class_definition | 6,420 | 6,998 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,359 |
class MllamaVisionAttention(nn.Module):
def __init__(self, config: MllamaVisionConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.num_heads = config.attention_heads
self.head_dim = config.hidden_size // config.attention_heads
self.q_proj = nn.Linear(self.e... | class_definition | 7,001 | 9,106 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,360 |
class MllamaVisionSdpaAttention(MllamaVisionAttention):
# Adapted from MllamaVisionAttention
def forward(
self,
hidden_state: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: bool = None,
) -> torch.Tensor:
# TODO: Improve this warning w... | class_definition | 9,109 | 11,083 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,361 |
class MllamaVisionEncoderLayer(nn.Module):
def __init__(self, config: MllamaVisionConfig, is_gated: bool = False):
super().__init__()
self.hidden_size = config.hidden_size
self.num_attention_heads = config.attention_heads
self.is_gated = is_gated
self.intermediate_size = con... | class_definition | 11,190 | 12,961 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,362 |
class MllamaVisionEncoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`MllamaEncoderLayer`].
Args:
config: MllamaConfig
"""
def __init__(self, config: MllamaVisionConfig, num_layers=32, is_gated=False):
su... | class_definition | 12,964 | 16,907 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,363 |
class MllamaTextRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
MllamaTextRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_state... | class_definition | 17,001 | 17,731 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,364 |
class MllamaTextCrossAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
config: Optional[MllamaTextConfig] = None,
layer_idx: Optional[int] = None,
):
super().__init__()
self.config = config
self.num_... | class_definition | 17,734 | 21,995 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,365 |
class MllamaTextCrossSdpaAttention(MllamaTextCrossAttention):
"""
Mllama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`MllamaTextCrossAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA A... | class_definition | 21,998 | 26,857 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,366 |
class MllamaTextSelfAttention(nn.Module):
def __init__(self, config: MllamaTextConfig, layer_idx: int):
super().__init__()
self.config = config
self.num_heads = config.num_attention_heads
self.dropout = config.dropout
self.hidden_size = config.hidden_size
self.num_key... | class_definition | 29,402 | 32,732 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,367 |
class MllamaTextSelfSdpaAttention(MllamaTextSelfAttention):
# Adapted from MllamaTextSelfAttention
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
position_embeddings: torch.Tensor,
output_attentions: bool = False,
use_cache: bool = F... | class_definition | 32,735 | 36,778 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,368 |
class MllamaTextMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = config.intermediate_size
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
... | class_definition | 37,090 | 37,785 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,369 |
class MllamaSelfAttentionDecoderLayer(nn.Module):
def __init__(self, config: MllamaTextConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = MLLAMA_TEXT_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
s... | class_definition | 37,863 | 41,876 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,370 |
class MllamaCrossAttentionDecoderLayer(torch.nn.Module):
"""Cross-attention transformer block with tanh-gated attention and feedforward."""
def __init__(self, config: MllamaTextConfig, layer_idx: int) -> None:
super().__init__()
self.layer_idx = layer_idx
self.cross_attn = MLLAMA_TEXT_C... | class_definition | 41,879 | 44,400 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,371 |
class MllamaRotaryEmbedding(nn.Module):
def __init__(self, config: MllamaTextConfig, device=None):
super().__init__()
self.rope_type = config.rope_scaling["rope_type"]
self.max_seq_len_cached = config.max_position_embeddings
self.original_max_seq_len = config.max_position_embeddings
... | class_definition | 44,403 | 47,176 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,372 |
class MllamaPreTrainedModel(PreTrainedModel):
config_class = MllamaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = [
"MllamaVisionEncoderLayer",
"MllamaCrossAttentionDecoderLayer",
"MllamaSelfAttentionDecoderLayer",
]
_support... | class_definition | 47,179 | 54,697 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,373 |
class MllamaVisionModel(MllamaPreTrainedModel):
config_class = MllamaVisionConfig
base_model_prefix = "vision_model"
def __init__(self, config: MllamaVisionConfig):
super().__init__(config)
self.image_size = config.image_size
self.patch_size = config.patch_size
self.max_num_... | class_definition | 70,201 | 79,315 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,374 |
class MllamaTextModel(MllamaPreTrainedModel):
config_class = MllamaTextConfig
base_model_prefix = "language_model.model"
def __init__(self, config: MllamaTextConfig):
super().__init__(config)
self.padding_idx = config.pad_token_id
self.vocab_size = config.vocab_size
self.emb... | class_definition | 79,472 | 87,185 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,375 |
class MllamaForCausalLM(MllamaPreTrainedModel, GenerationMixin):
config_class = MllamaTextConfig
_supports_static_cache = True # only the LLM without cross attn can do compile
base_model_prefix = "language_model"
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super()._... | class_definition | 87,312 | 92,995 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,376 |
class MllamaForConditionalGeneration(MllamaPreTrainedModel, GenerationMixin):
_supports_quantized_cache = False # quant cache not supported in encoder-decoder setting
def __init__(self, config: MllamaConfig):
super().__init__(config)
self.vocab_size = config.text_config.vocab_size
self... | class_definition | 93,136 | 105,369 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/mllama/modeling_mllama.py | null | 3,377 |
class OmDetTurboTextKwargs(TextKwargs, total=False):
task: Optional[Union[str, List[str], TextInput, PreTokenizedInput]] | class_definition | 1,264 | 1,388 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py | null | 3,378 |
class OmDetTurboProcessorKwargs(ProcessingKwargs, total=False):
text_kwargs: OmDetTurboTextKwargs
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": "max_length",
"truncation": True,
"max_length": 77,
"stride": 0,
... | class_definition | 1,518 | 2,149 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py | null | 3,379 |
class DictWithDeprecationWarning(dict):
message = (
"The `classes` key is deprecated for `OmDetTurboProcessor.post_process_grounded_object_detection` "
"output dict and will be removed in a 4.51.0 version. Please use `text_labels` instead."
)
def __getitem__(self, key):
if key == "c... | class_definition | 2,152 | 2,867 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py | null | 3,380 |
class OmDetTurboProcessor(ProcessorMixin):
r"""
Constructs a OmDet-Turbo processor which wraps a Deformable DETR image processor and an AutoTokenizer into a
single processor.
[`OmDetTurboProcessor`] offers all the functionalities of [`DetrImageProcessor`] and
[`AutoTokenizer`]. See the docstring of... | class_definition | 7,727 | 17,374 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/processing_omdet_turbo.py | null | 3,381 |
class OmDetTurboEncoderOutput(ModelOutput):
"""
Base class for outputs of the OmDetTurboHybridEncoder.
Args:
last_hidden_state (`torch.FloatTensor`):
Last hidden states of the encoder.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=Tru... | class_definition | 1,705 | 3,257 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,382 |
class OmDetTurboDecoderOutput(ModelOutput):
"""
Base class for outputs of the OmDetTurboDecoder.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the decoder.
decoder... | class_definition | 3,271 | 5,936 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,383 |
class OmDetTurboObjectDetectionOutput(ModelOutput):
"""
Output type of [`OmDetTurboObjectDetectionOutput`].
Args:
loss (`torch.FloatTensor`):
The loss value.
decoder_coord_logits (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`):
The predicted coordinates... | class_definition | 5,950 | 9,633 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,384 |
class OmDetTurboLRUCache:
def __init__(self, capacity: int):
self.cache = OrderedDict()
self.capacity = capacity
self.current_load = 0
def has(self, key) -> bool:
return key in self.cache
def get(self, key):
"""
Get the value of the key if the key exists in ... | class_definition | 12,780 | 13,887 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,385 |
class OmDetTurboLanguageBackbone(nn.Module):
def __init__(self, config: OmDetTurboConfig):
super().__init__()
self.model = AutoModel.from_config(config.text_config)
self.text_projection = nn.Parameter(torch.zeros(config.text_projection_in_dim, config.text_projection_out_dim))
def forwar... | class_definition | 13,890 | 15,066 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,386 |
class OmDetTurboVisionBackbone(nn.Module):
def __init__(self, config: OmDetTurboConfig):
super().__init__()
self.apply_layernorm_after_vision_backbone = config.apply_layernorm_after_vision_backbone
self.vision_backbone = load_backbone(config)
self.layer_norms = nn.ModuleList(
... | class_definition | 15,069 | 15,872 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,387 |
class MultiScaleDeformableAttentionFunction(Function):
@staticmethod
def forward(
context,
value,
value_spatial_shapes,
value_level_start_index,
sampling_locations,
attention_weights,
im2col_step,
):
context.im2col_step = im2col_step
ou... | class_definition | 15,988 | 17,432 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,388 |
class OmDetTurboMultiscaleDeformableAttention(nn.Module):
"""
Multiscale deformable attention as proposed in Deformable DETR.
"""
def __init__(self, config: OmDetTurboConfig, num_heads: int, n_points: int):
super().__init__()
kernel_loaded = MultiScaleDeformableAttention is not None
... | class_definition | 17,616 | 23,273 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,389 |
class OmDetTurboConvNormLayer(nn.Module):
def __init__(self, config, in_channels, out_channels, kernel_size, stride, padding=None, activation=None):
super().__init__()
self.conv = nn.Conv2d(
in_channels,
out_channels,
kernel_size,
stride,
p... | class_definition | 23,379 | 24,169 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,390 |
class OmDetTurboRepVggBlock(nn.Module):
"""
RepVGG architecture block introduced by the work "RepVGG: Making VGG-style ConvNets Great Again".
"""
def __init__(self, config: OmDetTurboConfig):
super().__init__()
activation = config.csp_activation
hidden_channels = int(config.enc... | class_definition | 24,310 | 25,072 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,391 |
class OmDetTurboCSPRepLayer(nn.Module):
"""
Cross Stage Partial (CSP) network layer with RepVGG blocks.
"""
def __init__(self, config: OmDetTurboConfig):
super().__init__()
in_channels = config.encoder_hidden_dim * 2
out_channels = config.encoder_hidden_dim
num_blocks =... | class_definition | 25,213 | 26,503 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,392 |
class OmDetTurboMultiheadAttention(nn.Module):
"""Equivalent implementation of nn.MultiheadAttention with `batch_first=True`."""
def __init__(self, config, hidden_size, num_attention_heads, dropout):
super().__init__()
if hidden_size % num_attention_heads != 0:
raise ValueError(
... | class_definition | 26,506 | 29,352 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,393 |
class OmDetTurboEncoderLayer(nn.Module):
def __init__(self, config: OmDetTurboConfig):
super().__init__()
self.self_attn = OmDetTurboMultiheadAttention(
config,
hidden_size=config.encoder_hidden_dim,
num_attention_heads=config.num_attention_heads,
drop... | class_definition | 29,355 | 32,778 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,394 |
class OmDetTurboEncoder(nn.Module):
def __init__(self, config: OmDetTurboConfig):
super().__init__()
self.layers = nn.ModuleList([OmDetTurboEncoderLayer(config) for _ in range(config.encoder_layers)])
def forward(
self, src, src_mask=None, pos_embed=None, output_attentions: bool = Fals... | class_definition | 32,781 | 33,677 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,395 |
class OmDetTurboHybridEncoder(nn.Module):
"""
Encoder consisting of channel projection layers, a set of `OmDetTurboEncoder`, a top-down Feature Pyramid Network
(FPN) and a bottom-up Path Aggregation Network (PAN). More details on the paper: https://arxiv.org/abs/2304.08069
Args:
config: OmDetTu... | class_definition | 33,680 | 41,907 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,396 |
class OmDetTurboMLPWithDropout(nn.Module):
def __init__(self, config):
super().__init__()
self.linear1 = nn.Linear(config.class_embed_dim, config.task_encoder_hidden_dim)
self.activation = ACT2FN[config.decoder_activation]
self.dropout = nn.Dropout(config.decoder_dropout)
sel... | class_definition | 41,910 | 42,410 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,397 |
class OmDetTurboMLP(nn.Module):
"""Very simple multi-layer perceptron (also called FFN)"""
def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
super().__init__()
self.num_layers = num_layers
hidden_layers_dims = [hidden_dim] * (num_layers - 1)
layers_dims = [input... | class_definition | 42,413 | 43,090 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,398 |
class OmDetTurboResidualLayer(nn.Module):
"""
A residual connection followed by a layer norm.
"""
def __init__(self, config):
super().__init__()
self.norm1 = nn.LayerNorm(config.class_embed_dim, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.decoder_dropout)
de... | class_definition | 43,093 | 43,482 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py | null | 3,399 |
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