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 YolosOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
)
... | class_definition | 7,098 | 7,570 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yolos/configuration_yolos.py | null | 7,900 |
class YolosImageProcessor(BaseImageProcessor):
r"""
Constructs a Detr image processor.
Args:
format (`str`, *optional*, defaults to `"coco_detection"`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
do_resize (`bool`, *optional*, defaults to `True`)... | class_definition | 26,350 | 67,890 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/yolos/image_processing_yolos.py | null | 7,901 |
class OPTConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`OPTModel`]. It is used to instantiate a OPT model
according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configur... | class_definition | 804 | 6,659 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/configuration_opt.py | null | 7,902 |
class TFOPTLearnedPositionalEmbedding(keras.layers.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs):
# OPT is set up so that if padding_idx is specified then offset the embedding ids by... | class_definition | 2,999 | 4,013 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_tf_opt.py | null | 7,903 |
class TFOPTAttention(keras.layers.Layer):
"""Multi-headed attention from "Attention Is All You Need"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
**kwargs,
):
super().... | class_definition | 4,103 | 11,676 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_tf_opt.py | null | 7,904 |
class TFOPTDecoderLayer(keras.layers.Layer):
def __init__(self, config: OPTConfig, **kwargs):
super().__init__(**kwargs)
self.do_layer_norm_before = config.do_layer_norm_before
self.embed_dim = config.hidden_size
self.self_attn = TFOPTAttention(
embed_dim=self.embed_dim,
... | class_definition | 11,679 | 16,740 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_tf_opt.py | null | 7,905 |
class TFOPTPreTrainedModel(TFPreTrainedModel):
"""
TFOPT Pretrained Model that inheritates from transformers.TFPreTrainedModel
Args:
config: OPTConfig
"""
config_class = OPTConfig
base_model_prefix = "model" | class_definition | 19,334 | 19,575 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_tf_opt.py | null | 7,906 |
class TFOPTDecoder(keras.layers.Layer):
config_class = OPTConfig
def __init__(self, config: OPTConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.padding_idx = config.pad_token_id
self.layerdrop = config.layerdrop
num_embeddings = config.max_position... | class_definition | 22,508 | 34,989 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_tf_opt.py | null | 7,907 |
class TFOPTMainLayer(keras.layers.Layer):
config_class = OPTConfig
def __init__(self, config: OPTConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.decoder = TFOPTDecoder(config, name="decoder")
def get_input_embeddings(self):
return self.decoder.embed_... | class_definition | 35,012 | 37,563 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_tf_opt.py | null | 7,908 |
class TFOPTModel(TFOPTPreTrainedModel):
config_class = OPTConfig
def __init__(self, config: OPTConfig, **kwargs):
super().__init__(config, **kwargs)
self.config = config
self.model = TFOPTMainLayer(config, name="model")
def get_input_embeddings(self):
return self.model.deco... | class_definition | 37,728 | 41,083 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_tf_opt.py | null | 7,909 |
class TFOPTForCausalLM(TFOPTPreTrainedModel, TFCausalLanguageModelingLoss):
config_class = OPTConfig
def __init__(self, config: OPTConfig, **kwargs):
super().__init__(config, **kwargs)
self.config = config
self.model = TFOPTMainLayer(config, name="model")
def get_output_embeddings(... | class_definition | 41,241 | 49,551 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_tf_opt.py | null | 7,910 |
class FlaxOPTAttention(nn.Module):
config: OPTConfig
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
bias: bool = True
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self) -> None:
self.head_dim = self.embed_dim // self.num_heads... | class_definition | 5,300 | 12,691 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_flax_opt.py | null | 7,911 |
class FlaxOPTDecoderLayer(nn.Module):
config: OPTConfig
dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.embed_dim = self.config.hidden_size
self.self_attn = FlaxOPTAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config... | class_definition | 12,694 | 15,818 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_flax_opt.py | null | 7,912 |
class FlaxOPTDecoderLayerCollection(nn.Module):
config: OPTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxOPTDecoderLayer(self.config, name=str(i), dtype=self.dtype)
for i in range(self.config.num_hidden_layers)
... | class_definition | 15,821 | 17,219 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_flax_opt.py | null | 7,913 |
class FlaxOPTLearnedPositionalEmbedding(nn.Embed):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def setup(self):
self.offset = 2
self.embedding = self.param(
"embedding", self.embedding_init, (self.num_embeddings + self.offset, self.features),... | class_definition | 17,222 | 17,729 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_flax_opt.py | null | 7,914 |
class FlaxOPTDecoder(nn.Module):
config: OPTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
offset: int = 2
def setup(self):
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
embed_dim = self.config.hidden_size
self.padding_idx = self.config.pad_to... | class_definition | 17,732 | 21,096 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_flax_opt.py | null | 7,915 |
class FlaxOPTPreTrainedModel(FlaxPreTrainedModel):
config_class = OPTConfig
base_model_prefix: str = "model"
module_class: nn.Module = None
def __init__(
self,
config: OPTConfig,
input_shape: Tuple[int] = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
... | class_definition | 21,099 | 26,303 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_flax_opt.py | null | 7,916 |
class FlaxOPTModule(nn.Module):
config: OPTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.decoder = FlaxOPTDecoder(self.config, dtype=self.dtype)
def _get_decoder_module(self):
return self.decoder
def __call__(
self,
inp... | class_definition | 26,306 | 27,533 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_flax_opt.py | null | 7,917 |
class FlaxOPTModel(FlaxOPTPreTrainedModel):
config: OPTConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
module_class = FlaxOPTModule | class_definition | 27,623 | 27,788 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_flax_opt.py | null | 7,918 |
class FlaxOPTForCausalLMModule(nn.Module):
config: OPTConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.model = FlaxOPTModule(config=self.config, dtype=self.dtype)
self.lm_head = nn.Dense(
self.config.vocab_size,
use_bias=False,
dtype=self.dtype... | class_definition | 28,046 | 29,673 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_flax_opt.py | null | 7,919 |
class FlaxOPTForCausalLM(FlaxOPTPreTrainedModel):
module_class = FlaxOPTForCausalLMModule
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 = self... | class_definition | 29,885 | 31,410 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_flax_opt.py | null | 7,920 |
class OPTLearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional embeddings up to a fixed maximum size.
"""
def __init__(self, num_embeddings: int, embedding_dim: int):
# OPT is set up so that if padding_idx is specified then offset the embedding ids by 2
# and adjus... | class_definition | 2,022 | 3,104 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_opt.py | null | 7,921 |
class OPTAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
config: OPTConfig,
is_decoder: bool = False,
**kwargs,
):
super().__init__()
self.config = config
self.embed_dim = config.hidden_siz... | class_definition | 3,107 | 10,840 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_opt.py | null | 7,922 |
class OptFlashAttention2(OPTAttention):
"""
OPT flash attention module. This module inherits from `OPTAttention` as the weights of the module stays untouched.
The only required change would be on the forward pass where it needs to correctly call the public API of flash
attention and deal with padding to... | class_definition | 10,843 | 16,919 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_opt.py | null | 7,923 |
class OPTSdpaAttention(OPTAttention):
"""
OPT sdpa attention module. This module inherits from `OPTAttention` as the weights of the module stays untouched.
The only required change would be on the forward pass where it needs to correctly call the public API of sdpa
attention and deal with padding tokens... | class_definition | 16,922 | 21,890 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_opt.py | null | 7,924 |
class OPTDecoderLayer(nn.Module):
def __init__(self, config: OPTConfig):
super().__init__()
self.embed_dim = config.hidden_size
self.self_attn = OPT_ATTENTION_CLASSES[config._attn_implementation](config=config, is_decoder=True)
self.do_layer_norm_before = config.do_layer_norm_befor... | class_definition | 22,025 | 26,348 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_opt.py | null | 7,925 |
class OPTPreTrainedModel(PreTrainedModel):
config_class = OPTConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["OPTDecoderLayer"]
_supports_flash_attn_2 = True
_supports_sdpa = True
def _init_weights(self, module):
std = self.config.init_... | class_definition | 27,362 | 28,081 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_opt.py | null | 7,926 |
class OPTDecoder(OPTPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`OPTDecoderLayer`]
Args:
config: OPTConfig
"""
def __init__(self, config: OPTConfig):
super().__init__(config)
self.dropout = config.dropout
... | class_definition | 32,283 | 45,899 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_opt.py | null | 7,927 |
class OPTModel(OPTPreTrainedModel):
def __init__(self, config: OPTConfig):
super().__init__(config)
self.decoder = OPTDecoder(config)
# Initialize weights and apply final processing
self.post_init()
def get_input_embeddings(self):
return self.decoder.embed_tokens
de... | class_definition | 46,041 | 48,710 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_opt.py | null | 7,928 |
class OPTForCausalLM(OPTPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.model = OPTModel(config)
# the lm_head weight is automatically tied to the embed tokens weight
self.lm_head = nn.Linear(c... | class_definition | 48,713 | 57,635 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_opt.py | null | 7,929 |
class OPTForSequenceClassification(OPTPreTrainedModel):
def __init__(self, config: OPTConfig):
super().__init__(config)
self.num_labels = config.num_labels
self.model = OPTModel(config)
self.score = nn.Linear(config.word_embed_proj_dim, self.num_labels, bias=False)
# Initial... | class_definition | 58,422 | 63,772 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_opt.py | null | 7,930 |
class OPTForQuestionAnswering(OPTPreTrainedModel):
def __init__(self, config: OPTConfig):
super().__init__(config)
self.model = OPTModel(config)
self.qa_outputs = nn.Linear(config.word_embed_proj_dim, 2)
# Initialize weights and apply final processing
self.post_init()
@... | class_definition | 64,073 | 69,712 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/opt/modeling_opt.py | null | 7,931 |
class NystromformerEmbeddings(nn.Module):
"""Construct the embeddings from word, position and token_type 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.positio... | class_definition | 1,588 | 4,563 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,932 |
class NystromformerSelfAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden... | class_definition | 4,566 | 10,561 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,933 |
class NystromformerSelfOutput(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 | 10,632 | 11,247 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,934 |
class NystromformerAttention(nn.Module):
def __init__(self, config, position_embedding_type=None):
super().__init__()
self.self = NystromformerSelfAttention(config, position_embedding_type=position_embedding_type)
self.output = NystromformerSelfOutput(config)
self.pruned_heads = set(... | class_definition | 11,250 | 12,780 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,935 |
class NystromformerIntermediate(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:
se... | class_definition | 12,878 | 13,452 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,936 |
class NystromformerOutput(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 | 13,544 | 14,161 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,937 |
class NystromformerLayer(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 = NystromformerAttention(config)
self.add_cross_attention = config.add_cross_attention
... | class_definition | 14,164 | 15,394 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,938 |
class NystromformerEncoder(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.layer = nn.ModuleList([NystromformerLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(
self,
hid... | class_definition | 15,397 | 17,285 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,939 |
class NystromformerPredictionHeadTransform(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:
... | class_definition | 17,394 | 18,103 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,940 |
class NystromformerLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = NystromformerPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.dec... | class_definition | 18,205 | 19,055 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,941 |
class NystromformerOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = NystromformerLMPredictionHead(config)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return predi... | class_definition | 19,152 | 19,484 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,942 |
class NystromformerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = NystromformerConfig
base_model_prefix = "nystromformer"
supports_gradient_checkpointing = True... | class_definition | 19,487 | 20,631 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,943 |
class NystromformerModel(NystromformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.embeddings = NystromformerEmbeddings(config)
self.encoder = NystromformerEncoder(config)
# Initialize weights and apply final processing
... | class_definition | 24,095 | 29,104 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,944 |
class NystromformerForMaskedLM(NystromformerPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder"]
def __init__(self, config):
super().__init__(config)
self.nystromformer = NystromformerModel(config)
self.cls = NystromformerOnlyMLMHead(config)
# Initialize weights ... | class_definition | 29,228 | 32,392 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,945 |
class NystromformerClassificationHead(nn.Module):
"""Head for sentence-level classification tasks."""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj... | class_definition | 32,395 | 33,080 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,946 |
class NystromformerForSequenceClassification(NystromformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.nystromformer = NystromformerModel(config)
self.classifier = NystromformerClassificationHead(config)
# In... | class_definition | 33,321 | 37,104 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,947 |
class NystromformerForMultipleChoice(NystromformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.nystromformer = NystromformerModel(config)
self.pre_classifier = nn.Linear(config.hidden_size, config.hidden_size)
self.classifier = nn.Linear(config.hidden_... | class_definition | 37,354 | 41,174 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,948 |
class NystromformerForTokenClassification(NystromformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.nystromformer = NystromformerModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classi... | class_definition | 41,422 | 44,180 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,949 |
class NystromformerForQuestionAnswering(NystromformerPreTrainedModel):
def __init__(self, config):
super().__init__(config)
config.num_labels = 2
self.num_labels = config.num_labels
self.nystromformer = NystromformerModel(config)
self.qa_outputs = nn.Linear(config.hidden_si... | class_definition | 44,486 | 48,757 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/modeling_nystromformer.py | null | 7,950 |
class NystromformerConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`NystromformerModel`]. It is used to instantiate
an Nystromformer model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the default... | class_definition | 805 | 6,365 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/nystromformer/configuration_nystromformer.py | null | 7,951 |
class DecisionTransformerGPT2Attention(nn.Module):
def __init__(self, config, is_cross_attention=False, layer_idx=None):
super().__init__()
self.config = config
max_positions = config.max_position_embeddings
self.register_buffer(
"bias",
torch.tril(torch.ones(... | class_definition | 5,840 | 15,369 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/decision_transformer/modeling_decision_transformer.py | null | 7,952 |
class DecisionTransformerGPT2MLP(nn.Module):
def __init__(self, intermediate_size, config):
super().__init__()
embed_dim = config.hidden_size
self.c_fc = Conv1D(intermediate_size, embed_dim)
self.c_proj = Conv1D(embed_dim, intermediate_size)
self.act = ACT2FN[config.activatio... | class_definition | 15,468 | 16,178 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/decision_transformer/modeling_decision_transformer.py | null | 7,953 |
class DecisionTransformerGPT2Block(nn.Module):
# Ignore copy
def __init__(self, config, layer_idx=None):
super().__init__()
hidden_size = config.hidden_size
inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size
self.ln_1 = nn.LayerNorm(hidden_size, eps=co... | class_definition | 16,279 | 19,814 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/decision_transformer/modeling_decision_transformer.py | null | 7,954 |
class DecisionTransformerGPT2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DecisionTransformerConfig
load_tf_weights = load_tf_weights_in_gpt2
base_model_prefi... | class_definition | 19,817 | 21,976 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/decision_transformer/modeling_decision_transformer.py | null | 7,955 |
class DecisionTransformerGPT2Model(DecisionTransformerGPT2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embed_dim = config.hidden_size
self.wte = nn.Embedding(config.vocab_size, self.embed_dim)
self.wpe = nn.Embedding(config.max_position_embeddings, se... | class_definition | 21,979 | 32,147 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/decision_transformer/modeling_decision_transformer.py | null | 7,956 |
class DecisionTransformerOutput(ModelOutput):
"""
Base class for model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the la... | class_definition | 32,161 | 34,057 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/decision_transformer/modeling_decision_transformer.py | null | 7,957 |
class DecisionTransformerPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = DecisionTransformerConfig
base_model_prefix = "decision_transformer"
main_input_name = "... | class_definition | 34,060 | 35,242 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/decision_transformer/modeling_decision_transformer.py | null | 7,958 |
class DecisionTransformerModel(DecisionTransformerPreTrainedModel):
"""
The model builds upon the GPT2 architecture to perform autoregressive prediction of actions in an offline RL
setting. Refer to the paper for more details: https://arxiv.org/abs/2106.01345
"""
def __init__(self, config):
... | class_definition | 37,000 | 44,241 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/decision_transformer/modeling_decision_transformer.py | null | 7,959 |
class DecisionTransformerConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`DecisionTransformerModel`]. It is used to
instantiate a Decision Transformer model according to the specified arguments, defining the model architecture.
Instantiating a configuration... | class_definition | 822 | 6,985 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/decision_transformer/configuration_decision_transformer.py | null | 7,960 |
class BeitFeatureExtractor(BeitImageProcessor):
def __init__(self, *args, **kwargs) -> None:
warnings.warn(
"The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use BeitImageProcessor instead.",
FutureWarning,
)... | class_definition | 809 | 1,171 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/feature_extraction_beit.py | null | 7,961 |
class BeitModelOutputWithPooling(BaseModelOutputWithPooling):
"""
Class for outputs of [`BeitModel`].
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 model.
pool... | class_definition | 1,926 | 3,458 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,962 |
class BeitDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob: Optional[float] = None) -> None:
super().__init__()
self.drop_prob = drop_prob
def forward(self, hidden_states: torch.Tensor) -> torch... | class_definition | 4,553 | 5,031 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,963 |
class BeitEmbeddings(nn.Module):
"""
Construct the CLS token, position and patch embeddings. Optionally, also the mask token.
"""
def __init__(self, config: BeitConfig) -> None:
super().__init__()
self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
if config.u... | class_definition | 5,189 | 9,491 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,964 |
class BeitPatchEmbeddings(nn.Module):
"""
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
Transformer.
"""
def __init__(self, config):
... | class_definition | 9,494 | 11,855 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,965 |
class BeitSelfAttention(nn.Module):
def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None) -> None:
super().__init__()
self.config = config
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
... | class_definition | 11,858 | 15,676 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,966 |
class BeitSdpaSelfAttention(BeitSelfAttention):
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
relative_position_bias: Optional["BeitRelativePositionBias"] = None,
interpolate_pos_encoding: bo... | class_definition | 15,679 | 18,608 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,967 |
class BeitSelfOutput(nn.Module):
"""
The residual connection is defined in BeitLayer instead of here (as is the case with other models), due to the
layernorm applied before each block.
"""
def __init__(self, config: BeitConfig) -> None:
super().__init__()
self.dense = nn.Linear(conf... | class_definition | 18,611 | 19,269 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,968 |
class BeitAttention(nn.Module):
def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None) -> None:
super().__init__()
self.attention = BEIT_SELF_ATTENTION_CLASSES[config._attn_implementation](config, window_size=window_size)
self.output = BeitSelfOutput(config)
self... | class_definition | 19,375 | 21,397 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,969 |
class BeitIntermediate(nn.Module):
def __init__(self, config: BeitConfig) -> None:
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:
... | class_definition | 21,400 | 21,986 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,970 |
class BeitOutput(nn.Module):
def __init__(self, config: BeitConfig) -> None:
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
... | class_definition | 21,989 | 22,436 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,971 |
class BeitLayer(nn.Module):
"""This corresponds to the Block class in the timm implementation."""
def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None, drop_path_rate: float = 0.0) -> None:
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
... | class_definition | 22,439 | 25,299 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,972 |
class BeitRelativePositionBias(nn.Module):
def __init__(self, config: BeitConfig, window_size: tuple) -> None:
super().__init__()
self.window_size = window_size
self.num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3
self.relative_position_bias_table = nn... | class_definition | 25,302 | 29,744 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,973 |
class BeitEncoder(nn.Module):
def __init__(self, config: BeitConfig, window_size: Optional[tuple] = None) -> None:
super().__init__()
self.config = config
if config.use_shared_relative_position_bias:
self.relative_position_bias = BeitRelativePositionBias(config, window_size=windo... | class_definition | 29,747 | 33,088 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,974 |
class BeitPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = BeitConfig
base_model_prefix = "beit"
main_input_name = "pixel_values"
supports_gradient_checkpoint... | class_definition | 33,091 | 34,403 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,975 |
class BeitModel(BeitPreTrainedModel):
def __init__(self, config: BeitConfig, add_pooling_layer: bool = True) -> None:
super().__init__(config)
self.config = config
self.embeddings = BeitEmbeddings(config)
self.encoder = BeitEncoder(config, window_size=self.embeddings.patch_embedding... | class_definition | 36,454 | 40,455 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,976 |
class BeitPooler(nn.Module):
def __init__(self, config: BeitConfig) -> None:
super().__init__()
self.layernorm = (
nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) if config.use_mean_pooling else None
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tenso... | class_definition | 40,458 | 41,173 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,977 |
class BeitForMaskedImageModeling(BeitPreTrainedModel):
def __init__(self, config: BeitConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.beit = BeitModel(config, add_pooling_layer=False)
# Classifier head
self.layernorm = nn.LayerNorm(config.... | class_definition | 41,697 | 45,744 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,978 |
class BeitForImageClassification(BeitPreTrainedModel):
def __init__(self, config: BeitConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.beit = BeitModel(config, add_pooling_layer=True)
# Classifier head
self.classifier = nn.Linear(config.hid... | class_definition | 45,992 | 49,578 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,979 |
class BeitConvModule(nn.Module):
"""
A convolutional block that bundles conv/norm/activation layers. This block simplifies the usage of convolution
layers, which are commonly used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU).
Based on OpenMMLab's implementation, found in https:... | class_definition | 49,581 | 50,772 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,980 |
class BeitPyramidPoolingBlock(nn.Module):
def __init__(self, pool_scale: int, in_channels: int, channels: int) -> None:
super().__init__()
self.layers = [
nn.AdaptiveAvgPool2d(pool_scale),
BeitConvModule(in_channels, channels, kernel_size=1),
]
for i, layer in... | class_definition | 50,775 | 51,361 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,981 |
class BeitPyramidPoolingModule(nn.Module):
"""
Pyramid Pooling Module (PPM) used in PSPNet.
Args:
pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid
Module.
in_channels (int): Input channels.
channels (int): Channels after modules, before conv_seg.
... | class_definition | 51,364 | 52,803 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,982 |
class BeitUperHead(nn.Module):
"""
Unified Perceptual Parsing for Scene Understanding. This head is the implementation of
[UPerNet](https://arxiv.org/abs/1807.10221).
Based on OpenMMLab's implementation, found in https://github.com/open-mmlab/mmsegmentation.
"""
def __init__(self, config: Beit... | class_definition | 52,806 | 55,996 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,983 |
class BeitFCNHead(nn.Module):
"""
Fully Convolution Networks for Semantic Segmentation. This head is implemented of
[FCNNet](https://arxiv.org/abs/1411.4038>).
Args:
config (BeitConfig): Configuration.
in_channels
kernel_size (int): The kernel size for convs in the head. Default... | class_definition | 55,999 | 58,252 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,984 |
class BeitForSemanticSegmentation(BeitPreTrainedModel):
def __init__(self, config: BeitConfig) -> None:
super().__init__(config)
self.num_labels = config.num_labels
self.beit = BeitModel(config, add_pooling_layer=False)
# FPNs
if len(self.config.out_indices) != 4:
... | class_definition | 58,420 | 64,727 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,985 |
class BeitBackbone(BeitPreTrainedModel, BackboneMixin):
def __init__(self, config):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)]
self.embeddings = BeitEmbeddings(config)
self.enc... | class_definition | 64,870 | 69,690 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_beit.py | null | 7,986 |
class BeitImageProcessor(BaseImageProcessor):
r"""
Constructs a BEiT 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 the
`do_resize` parameter in... | class_definition | 1,561 | 24,415 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/image_processing_beit.py | null | 7,987 |
class FlaxBeitModelOutputWithPooling(FlaxBaseModelOutputWithPooling):
"""
Class for outputs of [`FlaxBeitModel`].
Args:
last_hidden_state (`jnp.ndarray` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
... | class_definition | 1,412 | 2,906 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_flax_beit.py | null | 7,988 |
class FlaxBeitDropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
rate: float
@nn.module.compact
def __call__(self, inputs, deterministic: Optional[bool] = True):
if self.rate == 0.0:
return inputs
keep_prob = ... | class_definition | 7,161 | 7,956 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_flax_beit.py | null | 7,989 |
class FlaxBeitPatchEmbeddings(nn.Module):
config: BeitConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.num_channels = self.config.num_channels
image_size = self.config.image_size
patch_size = self.config.patch_size
num_patches = (im... | class_definition | 7,959 | 9,271 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_flax_beit.py | null | 7,990 |
class FlaxBeitEmbeddings(nn.Module):
"""Construct the CLS token, position and patch embeddings."""
config: BeitConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.cls_token = self.param("cls_token", nn.initializers.zeros, (1, 1, self.config.hidden_size))... | class_definition | 9,274 | 11,247 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_flax_beit.py | null | 7,991 |
class FlaxBeitRelativePositionBias(nn.Module):
config: BeitConfig
window_size: Tuple[int, int]
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
num_relative_distance = (2 * self.window_size[0] - 1) * (2 * self.window_size[1] - 1) + 3
self.relative_position... | class_definition | 11,250 | 12,282 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_flax_beit.py | null | 7,992 |
class FlaxBeitSelfAttention(nn.Module):
config: BeitConfig
window_size: Tuple[int, int]
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
if self.config.hidden_size % self.config.num_attention_heads != 0 and not hasattr(
self.config, "embedding_size"
... | class_definition | 12,285 | 15,632 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_flax_beit.py | null | 7,993 |
class FlaxBeitSelfOutput(nn.Module):
config: BeitConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=se... | class_definition | 15,635 | 16,274 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_flax_beit.py | null | 7,994 |
class FlaxBeitAttention(nn.Module):
config: BeitConfig
window_size: Tuple[int, int]
dtype: jnp.dtype = jnp.float32
def setup(self):
self.attention = FlaxBeitSelfAttention(self.config, self.window_size, dtype=self.dtype)
self.output = FlaxBeitSelfOutput(self.config, dtype=self.dtype)
... | class_definition | 16,277 | 17,139 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_flax_beit.py | null | 7,995 |
class FlaxBeitIntermediate(nn.Module):
config: BeitConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.intermediate_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
... | class_definition | 17,142 | 17,721 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_flax_beit.py | null | 7,996 |
class FlaxBeitOutput(nn.Module):
config: BeitConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.dense = nn.Dense(
self.config.hidden_size,
kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
dtype=self.d... | class_definition | 17,724 | 18,360 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_flax_beit.py | null | 7,997 |
class FlaxBeitLayer(nn.Module):
config: BeitConfig
window_size: Tuple[int, int]
drop_path_rate: float
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.attention = FlaxBeitAttention(self.config, self.window_size, dtype=self.dtype)
self.intermediate... | class_definition | 18,363 | 20,963 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_flax_beit.py | null | 7,998 |
class FlaxBeitLayerCollection(nn.Module):
config: BeitConfig
window_size: Tuple[int, int]
drop_path_rates: List[float]
relative_position_bias: Callable[[], jnp.ndarray]
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
def setup(self):
self.layers = [
FlaxBeitLa... | class_definition | 20,966 | 22,885 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/beit/modeling_flax_beit.py | null | 7,999 |
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