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 VisualBertSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 10,979 | 11,591 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,000 |
class VisualBertAttention(nn.Module):
def __init__(self, config):
super().__init__()
self.self = VisualBertSelfAttention(config)
self.output = VisualBertSelfOutput(config)
self.pruned_heads = set()
def prune_heads(self, heads):
if len(heads) == 0:
return
... | class_definition | 11,594 | 13,169 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,001 |
class VisualBertIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.... | class_definition | 13,264 | 13,835 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,002 |
class VisualBertOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 13,924 | 14,538 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,003 |
class VisualBertLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = VisualBertAttention(config)
self.intermediate = VisualBertIntermediate(config)
self.out... | class_definition | 14,541 | 15,830 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,004 |
class VisualBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([VisualBertLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hidden_st... | class_definition | 15,833 | 17,848 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,005 |
class VisualBertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# We "pool" the model by simply taking the... | class_definition | 17,937 | 18,502 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,006 |
class VisualBertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
sel... | class_definition | 18,608 | 19,314 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,007 |
class VisualBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = VisualBertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder =... | class_definition | 19,413 | 20,257 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,008 |
class VisualBertPreTrainingHeads(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = VisualBertLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.... | class_definition | 20,356 | 20,831 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,009 |
class VisualBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = VisualBertConfig
base_model_prefix = "visual_bert"
supports_gradient_checkpointing = True
de... | class_definition | 20,834 | 21,759 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,010 |
class VisualBertForPreTrainingOutput(ModelOutput):
"""
Output type of [`VisualBertForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the sentence-image predict... | class_definition | 21,773 | 23,737 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,011 |
class VisualBertModel(VisualBertPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. ... | class_definition | 28,623 | 36,886 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,012 |
class VisualBertForPreTraining(VisualBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.visual_bert = VisualBertModel(config)
self.cls = VisualBertPreTrainingHeads(confi... | class_definition | 37,134 | 43,745 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,013 |
class VisualBertForMultipleChoice(VisualBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.visual_bert = VisualBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.cls = nn.Linear(config.hidden_size, 1)
# Initialize weigh... | class_definition | 43,977 | 50,237 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,014 |
class VisualBertForQuestionAnswering(VisualBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.visual_bert = VisualBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.cls = nn.Linear(con... | class_definition | 50,453 | 55,478 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,015 |
class VisualBertForVisualReasoning(VisualBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.visual_bert = VisualBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.cls = nn.Linear(confi... | class_definition | 55,724 | 60,213 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,016 |
class VisualBertRegionToPhraseAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
... | class_definition | 60,216 | 62,103 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,017 |
class VisualBertForRegionToPhraseAlignment(VisualBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.visual_bert = VisualBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
sel... | class_definition | 62,337 | 68,914 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/visual_bert/modeling_visual_bert.py | null | 3,018 |
class VideoLlavaProcessor(ProcessorMixin):
r"""
Constructs a VideoLlava processor which wraps a VideoLlava image processor and a Llava tokenizer into a single processor.
[`VideoLlavaProcessor`] offers all the functionalities of [`VideoLlavaImageProcessor`] and [`LlamaTokenizerFast`]. See the
[`~VideoLl... | class_definition | 1,046 | 11,553 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/processing_video_llava.py | null | 3,019 |
class VideoLlavaCausalLMOutputWithPast(ModelOutput):
"""
Base class for VideoLlava causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
... | class_definition | 1,309 | 4,262 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py | null | 3,020 |
class VideoLlavaMultiModalProjector(nn.Module):
def __init__(self, config: VideoLlavaConfig):
super().__init__()
self.linear_1 = nn.Linear(
config.vision_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias
)
self.act = ACT2FN[config.p... | class_definition | 4,368 | 5,091 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py | null | 3,021 |
class VideoLlavaPreTrainedModel(PreTrainedModel):
config_class = VideoLlavaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["VideoLlavaVisionAttention"]
_skip_keys_device_placement = "past_key_values"
_supports_cache_class = True
_supports_flash_... | class_definition | 6,069 | 7,175 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py | null | 3,022 |
class VideoLlavaForConditionalGeneration(VideoLlavaPreTrainedModel, GenerationMixin):
def __init__(self, config: VideoLlavaConfig):
super().__init__(config)
self.video_tower = AutoModel.from_config(config.vision_config)
self.image_tower = AutoModel.from_config(config.vision_config)
... | class_definition | 12,801 | 34,523 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py | null | 3,023 |
class VideoLlavaImageProcessor(BaseImageProcessor):
r"""
Constructs a CLIP image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in the `pr... | class_definition | 2,068 | 19,310 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py | null | 3,024 |
class VideoLlavaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VideoLlavaForConditionalGeneration`]. It is used to instantiate an
VideoLlava model according to the specified arguments, defining the model architecture. Instantiating a configuration
with t... | class_definition | 888 | 6,298 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/configuration_video_llava.py | null | 3,025 |
class CohereLayerNorm(nn.Module):
def __init__(self, hidden_size=None, eps=1e-5, bias=False):
"""The hidden size can be a tuple or an int. The tuple is used for QKNorm to normalize across head_dim"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance... | class_definition | 1,757 | 2,562 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py | null | 3,026 |
class CohereRotaryEmbedding(LlamaRotaryEmbedding):
@torch.no_grad()
def forward(self, x, position_ids):
if "dynamic" in self.rope_type:
self._dynamic_frequency_update(position_ids, device=x.device)
# Core RoPE block
inv_freq_expanded = self.inv_freq[None, :, None].float().ex... | class_definition | 2,612 | 3,850 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py | null | 3,027 |
class CohereMLP(LlamaMLP):
def __init__(self, config):
super().__init__(config)
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... | class_definition | 5,694 | 6,050 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py | null | 3,028 |
class CohereAttention(LlamaAttention):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: CohereConfig, layer_idx: Optional[int] = None):
super().__init__(config, layer_idx)
self.use_qk_norm = config.use_qk_norm
if self.use_qk_norm:
... | class_definition | 6,053 | 9,436 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py | null | 3,029 |
class CohereDecoderLayer(nn.Module):
def __init__(self, config: CohereConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = CohereAttention(config=config, layer_idx=layer_idx)
self.mlp = CohereMLP(config)
self.input_layernorm = Coh... | class_definition | 9,439 | 12,787 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py | null | 3,030 |
class CohereModel(LlamaModel):
def __init__(self, config: CohereConfig):
super().__init__(config)
self.layers = nn.ModuleList(
[CohereDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.rotary_emb = CohereRotaryEmbedding(config=config)... | class_definition | 12,790 | 13,207 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py | null | 3,031 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 13,210 | 13,272 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py | null | 3,032 |
class CohereForCausalLM(LlamaForCausalLM):
def __init__(self, config):
super().__init__(config)
self.model = CohereModel(config)
self.logit_scale = config.logit_scale
self.tie_word_embeddings = config.tie_word_embeddings
def forward(
self,
input_ids: torch.LongTe... | class_definition | 13,275 | 17,665 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py | null | 3,033 |
class CohereTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a Cohere tokenizer. Based on byte-level Byte-Pair-Encoding.
This uses notably ByteFallback and NFC normalization.
```python
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c... | class_definition | 2,038 | 28,864 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py | null | 3,034 |
class CohereLayerNorm(nn.Module):
def __init__(self, hidden_size=None, eps=1e-5, bias=False):
"""The hidden size can be a tuple or an int. The tuple is used for QKNorm to normalize across head_dim"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance... | class_definition | 2,675 | 3,480 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modeling_cohere.py | null | 3,035 |
class CohereRotaryEmbedding(nn.Module):
def __init__(self, config: CohereConfig, 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", ... | class_definition | 3,483 | 6,741 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modeling_cohere.py | null | 3,036 |
class CohereMLP(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)
sel... | class_definition | 6,744 | 7,413 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modeling_cohere.py | null | 3,037 |
class CohereAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: CohereConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
self.head_dim = getattr(config, "he... | class_definition | 10,808 | 15,144 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modeling_cohere.py | null | 3,038 |
class CohereDecoderLayer(nn.Module):
def __init__(self, config: CohereConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = CohereAttention(config=config, layer_idx=layer_idx)
self.mlp = CohereMLP(config)
self.input_layernorm = Coh... | class_definition | 15,147 | 18,495 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modeling_cohere.py | null | 3,039 |
class CoherePreTrainedModel(PreTrainedModel):
config_class = CohereConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["CohereDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = True
_s... | class_definition | 19,521 | 20,447 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modeling_cohere.py | null | 3,040 |
class CohereModel(CoherePreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`CohereDecoderLayer`]
Args:
config: CohereConfig
"""
def __init__(self, config: CohereConfig):
super().__init__(config)
self.padding_idx = con... | class_definition | 25,254 | 36,506 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modeling_cohere.py | null | 3,041 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 36,509 | 36,571 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modeling_cohere.py | null | 3,042 |
class CohereForCausalLM(CoherePreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = CohereModel(config)
self.vocab_size = config.vocab_size
self.lm_he... | class_definition | 36,574 | 41,886 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modeling_cohere.py | null | 3,043 |
class CohereConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`CohereModel`]. It is used to instantiate an Cohere
model according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be... | class_definition | 1,117 | 10,543 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/configuration_cohere.py | null | 3,044 |
class UdopConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`UdopForConditionalGeneration`]. It is used to
instantiate a UDOP model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yi... | class_definition | 750 | 7,647 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/configuration_udop.py | null | 3,045 |
class UdopTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a "fast" UDOP tokenizer (backed by HuggingFace's *tokenizers* library). Adapted from
[`LayoutXLMTokenizer`] and [`T5Tokenizer`]. Based on
[BPE](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=BPE#models).
... | class_definition | 8,759 | 49,759 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py | null | 3,046 |
class UdopTextKwargs(TextKwargs, total=False):
word_labels: Optional[Union[List[int], List[List[int]]]]
boxes: Union[List[List[int]], List[List[List[int]]]] | class_definition | 1,000 | 1,164 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/processing_udop.py | null | 3,047 |
class UdopProcessorKwargs(ProcessingKwargs, total=False):
text_kwargs: UdopTextKwargs
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"truncation": False,
"stride": 0,
"return_overflowing_tokens": False,
... | class_definition | 1,167 | 1,680 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/processing_udop.py | null | 3,048 |
class UdopProcessor(ProcessorMixin):
r"""
Constructs a UDOP processor which combines a LayoutLMv3 image processor and a UDOP tokenizer into a single processor.
[`UdopProcessor`] offers all the functionalities you need to prepare data for the model.
It first uses [`LayoutLMv3ImageProcessor`] to resize,... | class_definition | 1,683 | 10,003 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/processing_udop.py | null | 3,049 |
class BaseModelOutputWithAttentionMask(ModelOutput):
"""
Class for the model's outputs that may also contain a past key/values (to speed up sequential decoding). Includes
an additional attention mask.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_si... | class_definition | 13,407 | 16,547 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,050 |
class UdopPatchEmbeddings(nn.Module):
"""2D Image to Patch Embeddings"""
def __init__(self, config):
super().__init__()
image_size, patch_size = config.image_size, config.patch_size
num_channels, hidden_size = config.num_channels, config.hidden_size
image_size = image_size if i... | class_definition | 21,067 | 22,416 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,051 |
class UdopPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models. Based on `T5PreTrainedModel`.
"""
config_class = UdopConfig
base_model_prefix = "transformer"
supports_gradient_checkpoint... | class_definition | 22,419 | 28,155 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,052 |
class UdopLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Construct a layernorm module in the Udop style. No bias and no subtraction of mean.
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
... | class_definition | 28,233 | 29,332 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,053 |
class UdopDenseActDense(nn.Module):
def __init__(self, config: UdopConfig):
super().__init__()
self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
self.dropout = nn.Dropout(config.dropout_rate)
self.act = A... | class_definition | 29,414 | 30,277 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,054 |
class UdopDenseGatedActDense(nn.Module):
def __init__(self, config: UdopConfig):
super().__init__()
self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wo = nn.Linear(config.d_ff, config.d_model, bias=Fals... | class_definition | 30,364 | 31,655 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,055 |
class UdopLayerFF(nn.Module):
def __init__(self, config: UdopConfig):
super().__init__()
if config.is_gated_act:
self.DenseReluDense = UdopDenseGatedActDense(config)
else:
self.DenseReluDense = UdopDenseActDense(config)
self.layer_norm = UdopLayerNorm(config.... | class_definition | 31,731 | 32,407 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,056 |
class UdopAttention(nn.Module):
def __init__(
self,
config: UdopConfig,
has_relative_attention_bias=False,
layer_idx: Optional[int] = None,
):
super().__init__()
self.is_decoder = config.is_decoder
self.has_relative_attention_bias = has_relative_attention_... | class_definition | 32,485 | 43,721 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,057 |
class UdopLayerSelfAttention(nn.Module):
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
super().__init__()
self.SelfAttention = UdopAttention(
config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx
)
... | class_definition | 43,808 | 45,164 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,058 |
class UdopLayerCrossAttention(nn.Module):
def __init__(self, config, layer_idx: Optional[int] = None):
super().__init__()
self.EncDecAttention = UdopAttention(config, has_relative_attention_bias=False, layer_idx=layer_idx)
self.layer_norm = UdopLayerNorm(config.d_model, eps=config.layer_norm... | class_definition | 45,252 | 46,671 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,059 |
class UdopBlock(nn.Module):
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
super().__init__()
self.is_decoder = config.is_decoder
self.layer = nn.ModuleList()
self.layer.append(
UdopLayerSelfAttention(
confi... | class_definition | 46,745 | 50,920 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,060 |
class UdopCellEmbeddings(nn.Module):
def __init__(self, max_2d_position_embeddings=501, hidden_size=1024):
super(UdopCellEmbeddings, self).__init__()
self.max_2d_position_embeddings = max_2d_position_embeddings
self.x_position_embeddings = nn.Embedding(max_2d_position_embeddings, hidden_siz... | class_definition | 50,923 | 52,006 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,061 |
class RelativePositionBiasBase(nn.Module, ABC):
"""
Base class of relative biases.
Args:
num_heads (`int`):
Number of attention heads in the model, it will create embeddings of size `num_heads`, which will be added to the scores of each token pair.
relative_attention_num_buckets... | class_definition | 52,235 | 57,026 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,062 |
class RelativePositionBias1D(RelativePositionBiasBase):
def __init__(self, scaling_factor=1, max_distance=128, **kwargs):
"""
Reimplementation of T5 relative position bias. Distance between given tokens is their distance in the sequence.
Parameters are the same as in base class
"""
... | class_definition | 57,029 | 57,864 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,063 |
class RelativePositionBiasHorizontal(RelativePositionBiasBase):
def __init__(self, scaling_factor=100, max_distance=100, **kwargs):
"""
Represents in the bucket embeddings horizontal distance between two tokens. Parameters are the same as in base
class
"""
super().__init__(sc... | class_definition | 57,867 | 58,810 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,064 |
class RelativePositionBiasVertical(RelativePositionBiasBase):
def __init__(self, scaling_factor=100, max_distance=100, **kwargs):
"""
Represents in the bucket embeddings vertical distance between two tokens. Parameters are the same as in base
class
"""
super().__init__(scalin... | class_definition | 58,813 | 59,742 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,065 |
class RelativePositionBiasAggregated(nn.Module):
def __init__(self, modules: Sequence[RelativePositionBiasBase]):
"""
Class which sums up various computed biases.
Args:
modules (Sequence[RelativePositionBiasBase]):
List of relative bias modules.
"""
... | class_definition | 59,745 | 60,423 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,066 |
class UdopStack(UdopPreTrainedModel):
"""
This class is based on `T5Stack`, but modified to take into account the image modality as well as 2D position
embeddings.
"""
def __init__(self, config, embed_tokens=None, embed_patches=None):
super().__init__(config)
self.embed_tokens = em... | class_definition | 61,540 | 79,659 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,067 |
class UdopModel(UdopPreTrainedModel):
_tied_weights_keys = [
"encoder.embed_tokens.weight",
"decoder.embed_tokens.weight",
"encoder.embed_patches.proj.weight",
"encoder.embed_patches.proj.bias",
"encoder.relative_bias.biases.0.relative_attention_bias.weight",
"decoder... | class_definition | 79,825 | 86,337 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,068 |
class UdopForConditionalGeneration(UdopPreTrainedModel, GenerationMixin):
_tied_weights_keys = [
"encoder.embed_tokens.weight",
"decoder.embed_tokens.weight",
"encoder.embed_patches.proj.weight",
"encoder.embed_patches.proj.bias",
"encoder.relative_bias.biases.0.relative_atte... | class_definition | 86,664 | 96,474 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,069 |
class UdopEncoderModel(UdopPreTrainedModel):
_tied_weights_keys = [
"encoder.embed_tokens.weight",
"encoder.embed_patches.proj.weight",
"encoder.embed_patches.proj.bias",
"encoder.relative_bias.biases.0.relative_attention_bias.weight",
]
def __init__(self, config: UdopConfig... | class_definition | 96,640 | 100,831 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py | null | 3,070 |
class UdopTokenizer(PreTrainedTokenizer):
"""
Adapted from [`LayoutXLMTokenizer`] and [`T5Tokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this supercla... | class_definition | 8,710 | 71,691 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop.py | null | 3,071 |
class Blip2VisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Blip2VisionModel`]. It is used to instantiate a
BLIP-2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration defaults will yield... | class_definition | 933 | 4,686 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py | null | 3,072 |
class Blip2QFormerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`Blip2QFormerModel`]. It is used to instantiate a
BLIP-2 Querying Transformer (Q-Former) model according to the specified arguments, defining the model architecture.
Instantiating a configur... | class_definition | 4,689 | 10,476 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py | null | 3,073 |
class Blip2Config(PretrainedConfig):
r"""
[`Blip2Config`] is the configuration class to store the configuration of a [`Blip2ForConditionalGeneration`]. It is
used to instantiate a BLIP-2 model according to the specified arguments, defining the vision model, Q-Former model
and language model configs. Ins... | class_definition | 10,479 | 15,940 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/configuration_blip_2.py | null | 3,074 |
class Blip2ForConditionalGenerationModelOutput(ModelOutput):
"""
Class defining the outputs of [`Blip2ForConditionalGeneration`].
Args:
loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
Language modeling loss from the langua... | class_definition | 1,713 | 3,138 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,075 |
class Blip2ImageTextMatchingModelOutput(ModelOutput):
"""
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Contrastive loss for image-text similarity.
logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_siz... | class_definition | 3,152 | 4,998 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,076 |
class Blip2TextModelOutput(ModelOutput):
"""
Base class for text model's outputs that also contains a pooling of the last hidden states.
Args:
text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
... | class_definition | 5,102 | 6,858 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,077 |
class Blip2VisionModelOutput(ModelOutput):
"""
Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
Args:
image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_pro... | class_definition | 6,964 | 8,749 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,078 |
class Blip2VisionEmbeddings(nn.Module):
def __init__(self, config: Blip2VisionConfig):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.class_embedding = nn.Parame... | class_definition | 8,843 | 12,255 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,079 |
class Blip2Attention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config):
super().__init__()
self.config = config
self.embed_dim = config.hidden_size
self.num_heads = config.num_attention_heads
self.head_dim = self.e... | class_definition | 12,258 | 15,492 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,080 |
class Blip2MLP(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_size)
... | class_definition | 15,556 | 16,127 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,081 |
class Blip2EncoderLayer(nn.Module):
def __init__(self, config: Blip2Config):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = Blip2Attention(config)
self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
self.mlp = Blip2MLP(config)
... | class_definition | 16,217 | 18,059 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,082 |
class Blip2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = Blip2Config
base_model_prefix = "blip"
supports_gradient_checkpointing = True
_no_split_modules =... | class_definition | 18,062 | 19,653 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,083 |
class Blip2Encoder(nn.Module):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`Blip2EncoderLayer`].
Args:
config (`Blip2Config`):
The corresponding vision configuration for the `Blip2Encoder`.
"""
def __init__(self, ... | class_definition | 30,034 | 33,771 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,084 |
class Blip2VisionModel(Blip2PreTrainedModel):
main_input_name = "pixel_values"
config_class = Blip2VisionConfig
def __init__(self, config: Blip2VisionConfig):
super().__init__(config)
self.config = config
embed_dim = config.hidden_size
self.embeddings = Blip2VisionEmbedding... | class_definition | 33,874 | 36,358 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,085 |
class Blip2QFormerMultiHeadAttention(nn.Module):
def __init__(self, config, is_cross_attention=False):
super().__init__()
self.config = config
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
"The... | class_definition | 36,361 | 42,924 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,086 |
class Blip2QFormerSelfOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 43,019 | 43,633 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,087 |
class Blip2QFormerAttention(nn.Module):
def __init__(self, config, is_cross_attention=False):
super().__init__()
self.attention = Blip2QFormerMultiHeadAttention(config, is_cross_attention)
self.output = Blip2QFormerSelfOutput(config)
self.pruned_heads = set()
def prune_heads(sel... | class_definition | 43,636 | 45,768 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,088 |
class Blip2QFormerIntermediate(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
sel... | class_definition | 45,865 | 46,438 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,089 |
class Blip2QFormerOutput(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
... | class_definition | 46,529 | 47,145 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,090 |
class Blip2QFormerLayer(nn.Module):
def __init__(self, config, layer_idx):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = Blip2QFormerAttention(config)
self.layer_idx = layer_idx
if layer_idx % ... | class_definition | 47,148 | 51,143 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,091 |
class Blip2QFormerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList(
[Blip2QFormerLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.gradient_checkpointing = False
... | class_definition | 51,146 | 54,638 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,092 |
class Blip2TextEmbeddings(nn.Module):
"""Construct the embeddings from word and position embeddings."""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = n... | class_definition | 54,641 | 56,350 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,093 |
class Blip2QFormerModel(Blip2PreTrainedModel):
"""
Querying Transformer (Q-Former), used in BLIP-2.
"""
def __init__(self, config: Blip2QFormerConfig):
super().__init__(config)
self.config = config
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
... | class_definition | 56,353 | 65,945 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,094 |
class Blip2Model(Blip2PreTrainedModel):
config_class = Blip2Config
main_input_name = "pixel_values"
def __init__(self, config: Blip2Config):
super().__init__(config)
self.vision_model = Blip2VisionModel(config.vision_config)
self.query_tokens = nn.Parameter(torch.zeros(1, config.n... | class_definition | 66,173 | 81,534 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,095 |
class Blip2TextModelWithProjection(Blip2PreTrainedModel):
supports_gradient_checkpointing = False
_keep_in_fp32_modules = []
def __init__(self, config: Blip2Config):
super().__init__(config)
self.query_tokens = nn.Parameter(torch.zeros(1, config.num_query_tokens, config.qformer_config.hidd... | class_definition | 81,706 | 85,111 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,096 |
class Blip2VisionModelWithProjection(Blip2PreTrainedModel):
main_input_name = "pixel_values"
_keep_in_fp32_modules = []
def __init__(self, config: Blip2Config):
super().__init__(config)
self.vision_model = Blip2VisionModel(config.vision_config)
self.query_tokens = nn.Parameter(tor... | class_definition | 85,285 | 89,318 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,097 |
class Blip2ForConditionalGeneration(Blip2PreTrainedModel, GenerationMixin):
config_class = Blip2Config
main_input_name = "pixel_values"
def __init__(self, config: Blip2Config):
super().__init__(config)
self.vision_model = Blip2VisionModel(config.vision_config)
self.query_tokens = ... | class_definition | 89,928 | 106,340 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,098 |
class Blip2ForImageTextRetrieval(Blip2PreTrainedModel):
main_input_name = "pixel_values"
_keep_in_fp32_modules = []
def __init__(self, config: Blip2Config):
super().__init__(config)
self.vision_model = Blip2VisionModel(config.vision_config)
self.query_tokens = nn.Parameter(torch.z... | class_definition | 106,666 | 114,259 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/blip_2/modeling_blip_2.py | null | 3,099 |
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