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 Emu3VQVAESpatialNorm(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
):
super().__init__()
self.norm_layer = nn.GroupNorm(
num_channels=out_channels,
num_groups=32,
eps=1e-6,
affine=True,
)... | class_definition | 17,481 | 18,491 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,500 |
class Emu3VQVAETemporalUpsample(nn.Module):
def __init__(
self,
in_channel: int,
out_channel: int,
):
super().__init__()
self.conv = Emu3VQVAEConv3d(
in_channel,
out_channel,
kernel_size=(3, 3, 3),
stride=(1, 1, 1),
... | class_definition | 18,494 | 19,334 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,501 |
class Emu3VQVAETemporalDownsample(nn.Module):
def __init__(
self,
in_channel: int,
out_channel: int,
):
super().__init__()
self.conv = Emu3VQVAEConv3d(
in_channel,
out_channel,
kernel_size=(4, 3, 3),
stride=(2, 1, 1),
... | class_definition | 19,337 | 19,791 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,502 |
class Emu3VQVAETemporalResnetBlock(nn.Module):
def __init__(
self,
in_channels,
out_channels=None,
):
super().__init__()
self.in_channels = in_channels
self.out_channels = in_channels if out_channels is None else out_channels
self.norm1 = nn.BatchNorm3d(i... | class_definition | 19,794 | 21,278 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,503 |
class Emu3VQVAEResnetBlock(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: Optional[int] = None,
quant_channels: Optional[int] = None,
):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None e... | class_definition | 21,281 | 23,380 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,504 |
class Emu3VQVAEAttentionBlock(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... | class_definition | 23,383 | 26,738 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,505 |
class Emu3VQVAEGroupNorm(nn.GroupNorm):
"""
Same as the torch GroupNorm with the only difference that this ones accepts
an optional kwarg `quant_states` which is not used. This class makes it easier to
use SpatialNorm or GroupNorm without conditionals
"""
def __init__(self, **kwargs):
s... | class_definition | 26,741 | 27,222 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,506 |
class Emu3VQVAEMiddleBlock(nn.Module):
def __init__(self, config, in_channels, quant_channels=None):
super().__init__()
self.block_1 = Emu3VQVAEResnetBlock(
in_channels=in_channels,
out_channels=in_channels,
quant_channels=quant_channels,
)
self.a... | class_definition | 27,225 | 28,727 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,507 |
class Emu3VQVAEDownBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.num_resolutions = len(config.channel_multiplier)
self.num_res_blocks = config.num_res_blocks
base_channels = config.base_channels
channel_multiplier = config.channel_multiplier
... | class_definition | 28,730 | 31,356 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,508 |
class Emu3VQVAEUpBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.num_resolutions = len(config.channel_multiplier)
self.num_res_blocks = config.num_res_blocks
quant_channels = config.embed_dim
block_in = config.base_channels * config.channel_multiplier[... | class_definition | 31,359 | 33,839 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,509 |
class Emu3VQVAEEncoder(nn.Module):
def __init__(self, config):
super().__init__()
base_channels = config.base_channels
in_channels = config.in_channels
double_latent = config.double_latent
latent_channels = config.latent_channels
channel_multiplier = config.channel_m... | class_definition | 33,842 | 36,449 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,510 |
class Emu3VQVAEDecoder(nn.Module):
def __init__(self, config: Emu3VQVAEConfig):
super().__init__()
quant_channels = config.embed_dim
block_in = config.base_channels * config.channel_multiplier[-1]
self.time_res_stack = nn.ModuleList()
for _ in range(config.num_res_blocks):
... | class_definition | 36,452 | 39,191 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,511 |
class Emu3VQVAE(PreTrainedModel):
config_class = Emu3VQVAEConfig
base_model_prefix = "emuvideovq"
main_input_name = "pixel_values"
_no_split_modules = [
"Emu3VQVAETemporalResnetBlock",
"Emu3VQVAEAttentionBlock",
"Emu3VQVAEResnetBlock",
"Emu3VQVAEVectorQuantizer",
]
... | class_definition | 40,463 | 44,357 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,512 |
class Emu3ImageVocabularyMapping:
"""
A class for mapping discrete image tokens from VQGAN to BPE tokens.
"""
def __init__(self, vocab_map):
self.vocab_map = vocab_map
self.eol_token_id = vocab_map.get("<|extra_200|>")
self.image_token_id = vocab_map.get("<image>")
@cached_... | class_definition | 44,360 | 46,339 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,513 |
class Emu3PreTrainedModel(PreTrainedModel):
config_class = Emu3Config
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = [
"Emu3DecoderLayer",
]
_skip_keys_device_placement = ["past_key_values", "causal_mask"]
_supports_flash_attn_2 = True
_sup... | class_definition | 47,357 | 48,475 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,514 |
class Emu3RotaryEmbedding(nn.Module):
def __init__(self, config: Emu3Config, 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", conf... | class_definition | 48,478 | 51,671 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,515 |
class Emu3TextModel(Emu3PreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Emu3TextDecoderLayer`]
Args:
config: Emu3TextConfig
"""
def __init__(self, config: Emu3Config):
super().__init__(config)
self.padding_idx = c... | class_definition | 56,476 | 67,704 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,516 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 67,707 | 67,769 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,517 |
class Emu3ForCausalLM(Emu3PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
config_class = Emu3TextConfig
def __init__(self, config):
super().__init__(config)
self.model = Emu3TextModel(config)
self.vocab_size = conf... | class_definition | 72,121 | 77,249 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,518 |
class Emu3ForConditionalGeneration(Emu3PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["text_model.lm_head.weight"]
def __init__(self, config):
super().__init__(config)
self.text_model = Emu3ForCausalLM._from_config(config.text_config)
self.vqmodel = Emu3VQVAE(config.vq_config)... | class_definition | 77,252 | 84,673 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/emu3/modeling_emu3.py | null | 3,519 |
class DiffLlamaMLP(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 | 2,724 | 3,396 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py | null | 3,520 |
class DiffLlamaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: DiffLlamaConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
... | class_definition | 5,816 | 11,350 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py | null | 3,521 |
class DiffLlamaFlashAttention2(DiffLlamaAttention):
"""
DiffLlama flash attention module. This module inherits from `DiffLlamaAttention` 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 ... | class_definition | 11,353 | 18,839 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py | null | 3,522 |
class DiffLlamaSdpaAttention(DiffLlamaAttention):
"""
DiffLlama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`DiffLlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
... | class_definition | 18,842 | 24,078 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py | null | 3,523 |
class DiffLlamaRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
DiffLlamaRMSNorm is equivalent to T5LayerNorm
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states)... | class_definition | 24,081 | 24,809 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py | null | 3,524 |
class DiffLlamaDecoderLayer(nn.Module):
def __init__(self, config: DiffLlamaConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = DIFFLLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
self.mlp = Dif... | class_definition | 24,968 | 27,100 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py | null | 3,525 |
class DiffLlamaPreTrainedModel(PreTrainedModel):
config_class = DiffLlamaConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["DiffLlamaDecoderLayer"]
_skip_keys_device_placement = ["past_key_values"]
_supports_flash_attn_2 = True
_supports_sdpa = Tr... | class_definition | 28,138 | 29,074 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py | null | 3,526 |
class DiffLlamaRotaryEmbedding(nn.Module):
def __init__(self, config: DiffLlamaConfig, 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_t... | class_definition | 29,077 | 32,280 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py | null | 3,527 |
class DiffLlamaModel(DiffLlamaPreTrainedModel):
"""
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DiffLlamaDecoderLayer`]
Args:
config: DiffLlamaConfig
"""
def __init__(self, config: DiffLlamaConfig):
super().__init__(config)
self.pa... | class_definition | 37,096 | 48,357 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py | null | 3,528 |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | class_definition | 48,360 | 48,422 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py | null | 3,529 |
class DiffLlamaForCausalLM(DiffLlamaPreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = DiffLlamaModel(config)
self.vocab_size = config.vocab_size
s... | class_definition | 48,425 | 53,501 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py | null | 3,530 |
class DiffLlamaForSequenceClassification(DiffLlamaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = DiffLlamaModel(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initiali... | class_definition | 54,306 | 58,134 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py | null | 3,531 |
class DiffLlamaForQuestionAnswering(DiffLlamaPreTrainedModel):
base_model_prefix = "transformer"
def __init__(self, config):
super().__init__(config)
self.transformer = DiffLlamaModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, 2)
# Initialize weights and apply fina... | class_definition | 58,438 | 61,846 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py | null | 3,532 |
class DiffLlamaForTokenClassification(DiffLlamaPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = DiffLlamaModel(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout = c... | class_definition | 62,101 | 65,329 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modeling_diffllama.py | null | 3,533 |
class DiffLlamaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`DiffLlamaModel`]. It is used to instantiate an DiffLlama
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults
will yield... | class_definition | 914 | 10,649 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/configuration_diffllama.py | null | 3,534 |
class DiffLlamaMLP(MistralMLP):
pass | class_definition | 1,607 | 1,647 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py | null | 3,535 |
class DiffLlamaAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: DiffLlamaConfig, layer_idx: Optional[int] = None):
super().__init__()
self.config = config
self.layer_idx = layer_idx
if layer_idx is None:
... | class_definition | 1,733 | 7,267 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py | null | 3,536 |
class DiffLlamaFlashAttention2(DiffLlamaAttention):
"""
DiffLlama flash attention module. This module inherits from `DiffLlamaAttention` 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 ... | class_definition | 7,270 | 14,756 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py | null | 3,537 |
class DiffLlamaSdpaAttention(DiffLlamaAttention):
"""
DiffLlama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
`DiffLlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
SDPA API.
"""
... | class_definition | 14,759 | 19,995 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py | null | 3,538 |
class DiffLlamaDecoderLayer(LlamaDecoderLayer):
def __init__(self, config: DiffLlamaConfig, layer_idx: int):
super().__init__(config, layer_idx)
self.self_attn = DIFFLLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) | class_definition | 20,154 | 20,429 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py | null | 3,539 |
class DiffLlamaPreTrainedModel(LlamaPreTrainedModel):
_supports_flex_attn = False | class_definition | 20,432 | 20,517 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py | null | 3,540 |
class DiffLlamaModel(LlamaModel):
pass | class_definition | 20,520 | 20,562 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py | null | 3,541 |
class DiffLlamaForCausalLM(GemmaForCausalLM):
pass | class_definition | 20,565 | 20,619 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py | null | 3,542 |
class DiffLlamaForSequenceClassification(LlamaForSequenceClassification):
pass | class_definition | 20,622 | 20,704 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py | null | 3,543 |
class DiffLlamaForQuestionAnswering(LlamaForQuestionAnswering):
pass | class_definition | 20,707 | 20,779 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py | null | 3,544 |
class DiffLlamaForTokenClassification(LlamaForTokenClassification):
pass | class_definition | 20,782 | 20,858 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/diffllama/modular_diffllama.py | null | 3,545 |
class SqueezeBertTokenizer(PreTrainedTokenizer):
r"""
Construct a SqueezeBERT tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
... | class_definition | 1,797 | 12,496 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py | null | 3,546 |
class BasicTokenizer:
"""
Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
Args:
do_lower_case (`bool`, *optional*, defaults to `True`):
Whether or not to lowercase the input when tokenizing.
never_split (`Iterable`, *opti... | class_definition | 12,571 | 19,319 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py | null | 3,547 |
class WordpieceTokenizer:
"""Runs WordPiece tokenization."""
def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""
Toke... | class_definition | 19,322 | 21,210 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert.py | null | 3,548 |
class SqueezeBertEmbeddings(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.embedding_size, padding_idx=config.pad_token_id)
self.positi... | class_definition | 1,446 | 3,447 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,549 |
class MatMulWrapper(nn.Module):
"""
Wrapper for torch.matmul(). This makes flop-counting easier to implement. Note that if you directly call
torch.matmul() in your code, the flop counter will typically ignore the flops of the matmul.
"""
def __init__(self):
super().__init__()
def forwa... | class_definition | 3,450 | 4,150 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,550 |
class SqueezeBertLayerNorm(nn.LayerNorm):
"""
This is a nn.LayerNorm subclass that accepts NCW data layout and performs normalization in the C dimension.
N = batch C = channels W = sequence length
"""
def __init__(self, hidden_size, eps=1e-12):
nn.LayerNorm.__init__(self, normalized_shape=... | class_definition | 4,153 | 4,669 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,551 |
class ConvDropoutLayerNorm(nn.Module):
"""
ConvDropoutLayerNorm: Conv, Dropout, LayerNorm
"""
def __init__(self, cin, cout, groups, dropout_prob):
super().__init__()
self.conv1d = nn.Conv1d(in_channels=cin, out_channels=cout, kernel_size=1, groups=groups)
self.layernorm = Squee... | class_definition | 4,672 | 5,257 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,552 |
class ConvActivation(nn.Module):
"""
ConvActivation: Conv, Activation
"""
def __init__(self, cin, cout, groups, act):
super().__init__()
self.conv1d = nn.Conv1d(in_channels=cin, out_channels=cout, kernel_size=1, groups=groups)
self.act = ACT2FN[act]
def forward(self, x):
... | class_definition | 5,260 | 5,641 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,553 |
class SqueezeBertSelfAttention(nn.Module):
def __init__(self, config, cin, q_groups=1, k_groups=1, v_groups=1):
"""
config = used for some things; ignored for others (work in progress...) cin = input channels = output channels
groups = number of groups to use in conv1d layers
"""
... | class_definition | 5,644 | 9,635 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,554 |
class SqueezeBertModule(nn.Module):
def __init__(self, config):
"""
- hidden_size = input chans = output chans for Q, K, V (they are all the same ... for now) = output chans for
the module
- intermediate_size = output chans for intermediate layer
- groups = number of groups... | class_definition | 9,638 | 11,494 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,555 |
class SqueezeBertEncoder(nn.Module):
def __init__(self, config):
super().__init__()
assert config.embedding_size == config.hidden_size, (
"If you want embedding_size != intermediate hidden_size, "
"please insert a Conv1d layer to adjust the number of channels "
"... | class_definition | 11,497 | 13,820 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,556 |
class SqueezeBertPooler(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):
# We "pool" the model by simply taking the hidden state corresponding
... | class_definition | 13,823 | 14,359 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,557 |
class SqueezeBertPredictionHeadTransform(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:
se... | class_definition | 14,362 | 15,039 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,558 |
class SqueezeBertLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = SqueezeBertPredictionHeadTransform(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 | 15,042 | 15,896 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,559 |
class SqueezeBertOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = SqueezeBertLMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores | class_definition | 15,899 | 16,197 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,560 |
class SqueezeBertPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SqueezeBertConfig
base_model_prefix = "transformer"
def _init_weights(self, module):
""... | class_definition | 16,200 | 17,303 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,561 |
class SqueezeBertModel(SqueezeBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.embeddings = SqueezeBertEmbeddings(config)
self.encoder = SqueezeBertEncoder(config)
self.pooler = SqueezeBertPooler(config)
# Initialize weights and apply final pr... | class_definition | 22,077 | 26,444 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,562 |
class SqueezeBertForMaskedLM(SqueezeBertPreTrainedModel):
_tied_weights_keys = ["cls.predictions.decoder.weight", "cls.predictions.decoder.bias"]
def __init__(self, config):
super().__init__(config)
self.transformer = SqueezeBertModel(config)
self.cls = SqueezeBertOnlyMLMHead(config)
... | class_definition | 26,563 | 29,706 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,563 |
class SqueezeBertForSequenceClassification(SqueezeBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.transformer = SqueezeBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_p... | class_definition | 29,942 | 33,824 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,564 |
class SqueezeBertForMultipleChoice(SqueezeBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.transformer = SqueezeBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
# Initia... | class_definition | 34,069 | 37,599 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,565 |
class SqueezeBertForTokenClassification(SqueezeBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = SqueezeBertModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = n... | class_definition | 37,842 | 40,543 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,566 |
class SqueezeBertForQuestionAnswering(SqueezeBertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.transformer = SqueezeBertModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
# Initi... | class_definition | 40,847 | 45,052 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/modeling_squeezebert.py | null | 3,567 |
class SqueezeBertConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SqueezeBertModel`]. It is used to instantiate a
SqueezeBERT model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will ... | class_definition | 890 | 6,640 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/configuration_squeezebert.py | null | 3,568 |
class SqueezeBertOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
else:
dynamic_axis = {0: "batch", 1: "sequence"}
return OrderedDict(
... | class_definition | 6,741 | 7,243 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/configuration_squeezebert.py | null | 3,569 |
class SqueezeBertTokenizerFast(PreTrainedTokenizerFast):
r"""
Construct a "fast" SqueezeBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this supercl... | class_definition | 1,167 | 7,818 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/squeezebert/tokenization_squeezebert_fast.py | null | 3,570 |
class SwinEncoderOutput(ModelOutput):
"""
Swin encoder's outputs, with potential hidden states and attentions.
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.
... | class_definition | 1,933 | 3,898 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,571 |
class SwinModelOutput(ModelOutput):
"""
Swin 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 last layer of the mode... | class_definition | 3,912 | 6,141 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,572 |
class SwinMaskedImageModelingOutput(ModelOutput):
"""
Swin masked image model outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
Masked image modeling (MLM) loss.
reconstruction (`torch.FloatTensor` of shape `(batc... | class_definition | 6,155 | 8,566 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,573 |
class SwinImageClassifierOutput(ModelOutput):
"""
Swin outputs for image classification.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`torch.FloatTensor` of ... | class_definition | 8,580 | 10,716 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,574 |
class SwinEmbeddings(nn.Module):
"""
Construct the patch and position embeddings. Optionally, also the mask token.
"""
def __init__(self, config, use_mask_token=False):
super().__init__()
self.patch_embeddings = SwinPatchEmbeddings(config)
num_patches = self.patch_embeddings.nu... | class_definition | 11,587 | 15,496 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,575 |
class SwinPatchEmbeddings(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 | 15,499 | 17,679 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,576 |
class SwinPatchMerging(nn.Module):
"""
Patch Merging Layer.
Args:
input_resolution (`Tuple[int]`):
Resolution of input feature.
dim (`int`):
Number of input channels.
norm_layer (`nn.Module`, *optional*, defaults to `nn.LayerNorm`):
Normalization ... | class_definition | 17,682 | 19,960 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,577 |
class SwinDropPath(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 | 21,200 | 21,678 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,578 |
class SwinSelfAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size):
super().__init__()
if dim % num_heads != 0:
raise ValueError(
f"The hidden size ({dim}) is not a multiple of the number of attention heads ({num_heads})"
)
se... | class_definition | 21,681 | 26,548 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,579 |
class SwinSelfOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, dim)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
hidden... | class_definition | 26,551 | 26,988 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,580 |
class SwinAttention(nn.Module):
def __init__(self, config, dim, num_heads, window_size):
super().__init__()
self.self = SwinSelfAttention(config, dim, num_heads, window_size)
self.output = SwinSelfOutput(config, dim)
self.pruned_heads = set()
def prune_heads(self, heads):
... | class_definition | 26,991 | 28,669 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,581 |
class SwinIntermediate(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(dim, int(config.mlp_ratio * dim))
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_... | class_definition | 28,672 | 29,230 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,582 |
class SwinOutput(nn.Module):
def __init__(self, config, dim):
super().__init__()
self.dense = nn.Linear(int(config.mlp_ratio * dim), dim)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.de... | class_definition | 29,233 | 29,652 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,583 |
class SwinLayer(nn.Module):
def __init__(self, config, dim, input_resolution, num_heads, drop_path_rate=0.0, shift_size=0):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.shift_size = shift_size
self.window_size = config.window_size
self... | class_definition | 29,655 | 35,323 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,584 |
class SwinStage(nn.Module):
def __init__(self, config, dim, input_resolution, depth, num_heads, drop_path, downsample):
super().__init__()
self.config = config
self.dim = dim
self.blocks = nn.ModuleList(
[
SwinLayer(
config=config,
... | class_definition | 35,326 | 37,540 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,585 |
class SwinEncoder(nn.Module):
def __init__(self, config, grid_size):
super().__init__()
self.num_layers = len(config.depths)
self.config = config
dpr = [x.item() for x in torch.linspace(0, config.drop_path_rate, sum(config.depths))]
self.layers = nn.ModuleList(
[
... | class_definition | 37,543 | 42,280 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,586 |
class SwinPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = SwinConfig
base_model_prefix = "swin"
main_input_name = "pixel_values"
supports_gradient_checkpoint... | class_definition | 42,283 | 43,234 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,587 |
class SwinModel(SwinPreTrainedModel):
def __init__(self, config, add_pooling_layer=True, use_mask_token=False):
super().__init__(config)
self.config = config
self.num_layers = len(config.depths)
self.num_features = int(config.embed_dim * 2 ** (self.num_layers - 1))
self.embe... | class_definition | 45,577 | 49,742 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,588 |
class SwinForMaskedImageModeling(SwinPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.swin = SwinModel(config, add_pooling_layer=False, use_mask_token=True)
num_features = int(config.embed_dim * 2 ** (config.num_layers - 1))
self.decoder = nn.Sequential(
... | class_definition | 50,151 | 54,849 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,589 |
class SwinForImageClassification(SwinPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.swin = SwinModel(config)
# Classifier head
self.classifier = (
nn.Linear(self.swin.num_features, config.num_label... | class_definition | 55,397 | 59,020 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,590 |
class SwinBackbone(SwinPreTrainedModel, BackboneMixin):
def __init__(self, config: SwinConfig):
super().__init__(config)
super()._init_backbone(config)
self.num_features = [config.embed_dim] + [int(config.embed_dim * 2**i) for i in range(len(config.depths))]
self.embeddings = SwinEm... | class_definition | 59,163 | 63,041 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_swin.py | null | 3,591 |
class SwinConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SwinModel`]. It is used to instantiate a Swin
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will ... | class_definition | 1,019 | 7,503 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/configuration_swin.py | null | 3,592 |
class SwinOnnxConfig(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 | 7,506 | 7,903 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/configuration_swin.py | null | 3,593 |
class TFSwinEncoderOutput(ModelOutput):
"""
Swin encoder's outputs, with potential hidden states and attentions.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
h... | class_definition | 1,946 | 3,816 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,594 |
class TFSwinModelOutput(ModelOutput):
"""
Swin model's outputs that also contains a pooling of the last hidden states.
Args:
last_hidden_state (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model.
... | class_definition | 3,830 | 5,945 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,595 |
class TFSwinMaskedImageModelingOutput(ModelOutput):
"""
Swin masked image model outputs.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
Masked image modeling (MLM) loss.
reconstruction (`tf.Tensor` of shape `(batch_size, num_ch... | class_definition | 5,959 | 8,256 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,596 |
class TFSwinImageClassifierOutput(ModelOutput):
"""
Swin outputs for image classification.
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`tf.Tensor` of shape `(batch_... | class_definition | 8,270 | 10,292 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,597 |
class TFSwinEmbeddings(keras.layers.Layer):
"""
Construct the patch and position embeddings. Optionally, also the mask token.
"""
def __init__(self, config: SwinConfig, use_mask_token: bool = False, **kwargs) -> None:
super().__init__(**kwargs)
self.patch_embeddings = TFSwinPatchEmbeddi... | class_definition | 12,147 | 15,071 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,598 |
class TFSwinPatchEmbeddings(keras.layers.Layer):
"""
Image to Patch Embedding.
"""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
image_size, patch_size = config.image_size, config.patch_size
num_channels, hidden_size = config.num_channels, config.embed_dim
... | class_definition | 15,074 | 18,104 | 0 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/swin/modeling_tf_swin.py | null | 3,599 |
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