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@add_start_docstrings_to_model_forward(STARCODER2_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTe... | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient check... | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
hidden_states = nn.functional.dropout(
hidden_states, p=self.embedding_dropout, training=self.training
) # m... | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=ca... | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
output = BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values if use_cache else None,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
return output if return_dict else output.to_tuple() | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
def _update_causal_mask(
self,
attention_mask: torch.Tensor,
input_tensor: torch.Tensor,
cache_position: torch.Tensor,
past_key_values: Cache,
output_attentions: bool,
):
if self.config._attn_implementation == "flash_attention_2":
if attention_mask... | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
# For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
# order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
# to infer the attention mask.
past_seen_tokens = past_key_values.get_seq_leng... | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
# When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
if (
self.config._attn_implementation == "sdpa"
and not (using_static_cache or using_sliding_window_cache)
and not output_attentions
):
if Atten... | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
dtype, device = input_tensor.dtype, input_tensor.device
min_dtype = torch.finfo(dtype).min
sequence_length = input_tensor.shape[1]
# SlidingWindowCache or StaticCache
if using_sliding_window_cache or using_static_cache:
target_length = past_key_values.get_max_cache_shape()
... | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
# In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
attention_mask,
sequence_length=sequence_length,
target_length=target_length,
dtype=dtype,
dev... | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
if (
self.config._attn_implementation == "sdpa"
and attention_mask is not None
and attention_mask.device.type == "cuda"
and not output_attentions
):
# Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows whe... | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
@staticmethod
def _prepare_4d_causal_attention_mask_with_cache_position(
attention_mask: torch.Tensor,
sequence_length: int,
target_length: int,
dtype: torch.dtype,
device: torch.device,
cache_position: torch.Tensor,
batch_size: int,
config: Starcoder2... | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
Args:
attention_mask (`torch.Tensor`):
A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
sequence_length (`int`):
The sequence length being processed.
target... | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
The model's configuration class
past_key_values (`Cache`):
The cache class that is being used currently to generate
"""
if attention_mask is not None and attention_mask.dim() == 4:
# In this case we assume that the mask comes already in inverted form and requires ... | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
sliding_attend_mask = torch.arange(target_length, device=device) <= (
cache_position.reshape(-1, 1) - config.sliding_window
)
diagonal_attend_mas... | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
padding_mask, min_dtype
)
return causal_mask | 3,230 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | 3,231 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
class Starcoder2ForCausalLM(Starcoder2PreTrainedModel, GenerationMixin):
_tied_weights_keys = ["lm_head.weight"]
_tp_plan = {"lm_head": "colwise_rep"}
def __init__(self, config):
super().__init__(config)
self.model = Starcoder2Model(config)
self.vocab_size = config.vocab_size
... | 3,232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
@add_start_docstrings_to_model_forward(STARCODER2_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Opt... | 3,232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
... | 3,232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
num_logits_to_keep (`int`, *optional*):
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, whic... | 3,232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
>>> # Generate
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
... | 3,232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
... | 3,232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_sta... | 3,232 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
class Starcoder2ForSequenceClassification(Starcoder2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Starcoder2Model(config)
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
# Initi... | 3,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
@add_start_docstrings_to_model_forward(STARCODER2_INPUTS_DOCSTRING)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[tor... | 3,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
transformer_outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
o... | 3,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
if self.config.pad_token_id is None and batch_size != 1:
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
if self.config.pad_token_id is None:
sequence_lengths = -1
else:
if input_ids is not None:
# if no pad token foun... | 3,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
if not return_dict:
output = (pooled_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutputWithPast(
loss=loss,
logits=pooled_logits,
past_key_values=transformer_outputs.past_key_va... | 3,233 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
class Starcoder2ForTokenClassification(Starcoder2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.model = Starcoder2Model(config)
if getattr(config, "classifier_dropout", None) is not None:
classifier_dropout ... | 3,234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
@add_start_docstrings_to_model_forward(STARCODER2_INPUTS_DOCSTRING)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=TokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
... | 3,234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
... | 3,234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
outputs = self.model(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden... | 3,234 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py |
class Starcoder2MLP(nn.Module):
def __init__(self, config: Starcoder2Config):
super().__init__()
embed_dim = config.hidden_size
self.c_fc = nn.Linear(embed_dim, config.intermediate_size, bias=config.use_bias)
self.c_proj = nn.Linear(config.intermediate_size, embed_dim, bias=config.us... | 3,235 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
class Starcoder2Attention(MistralAttention):
def __init__(self, config: Starcoder2Config, layer_idx: Optional[int] = None):
super().__init__()
self.residual_dropout = config.residual_dropout
self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.u... | 3,236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttenti... | 3,236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
if past_key_value is not None:
# sin and cos are specific to RoPE models; cache_position needed for the static cache
cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, ca... | 3,236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
attn_output, attn_weights = attention_interface(
self,
query_states,
key_states,
value_states,
attention_mask,
dropout=0.0 if not self.training else self.attention_dropout,
scaling=self.scaling,
sliding_window=getattr(self.c... | 3,236 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
class Starcoder2DecoderLayer(MistralDecoderLayer):
def __init__(self, config: Starcoder2Config, layer_idx: int):
super().__init__(self)
self.self_attn = Starcoder2Attention(config=config, layer_idx=layer_idx)
self.mlp = Starcoder2MLP(config)
self.input_layernorm = nn.LayerNorm(config... | 3,237 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
class Starcoder2Model(MistralModel):
def __init__(self, config: Starcoder2Config):
super().__init__(config)
self.layers = nn.ModuleList(
[Starcoder2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.norm = nn.LayerNorm(config.hidden_s... | 3,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
@add_start_docstrings_to_model_forward(STARCODER2_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTe... | 3,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict | 3,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if self.gradient_checkpointing and self.training and use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient check... | 3,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
causal_mask = self._update_causal_mask(
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
)
hidden_states = inputs_embeds
hidden_states = nn.functional.dropout(
hidden_states, p=self.embedding_dropout, training=self.training
) # m... | 3,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
layer_outputs = decoder_layer(
hidden_states,
attention_mask=causal_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=output_attentions,
use_cache=use_cache,
cache_position=ca... | 3,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
output = BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=past_key_values if use_cache else None,
hidden_states=all_hidden_states,
attentions=all_self_attns,
)
return output if return_dict else output.to_tuple() | 3,238 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
class Starcoder2ForCausalLM(MistralForCausalLM):
pass | 3,239 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
class Starcoder2ForSequenceClassification(MistralForSequenceClassification):
pass | 3,240 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
class Starcoder2ForTokenClassification(MistralForTokenClassification):
pass | 3,241 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/starcoder2/modular_starcoder2.py |
class VitsConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VitsModel`]. It is used to instantiate a VITS
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar confi... | 3,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py |
Args:
vocab_size (`int`, *optional*, defaults to 38):
Vocabulary size of the VITS model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed to the forward method of [`VitsModel`].
hidden_size (`int`, *optional*, defaults to 192):
... | 3,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py |
ffn_dim (`int`, *optional*, defaults to 768):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
layerdrop (`float`, *optional*, defaults to 0.1):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.... | 3,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py |
hidden_dropout (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings and encoder.
attention_dropout (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
activation_dropout (`float`, *... | 3,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py |
Number of speakers if this is a multi-speaker model.
speaker_embedding_size (`int`, *optional*, defaults to 0):
Number of channels used by the speaker embeddings. Is zero for single-speaker models.
upsample_initial_channel (`int`, *optional*, defaults to 512):
The number of input... | 3,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py |
network. The length of `upsample_kernel_sizes` defines the number of convolutional layers and has to match
the length of `upsample_rates`.
resblock_kernel_sizes (`Tuple[int]` or `List[int]`, *optional*, defaults to `[3, 7, 11]`):
A tuple of integers defining the kernel sizes of the 1D co... | 3,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py |
depth_separable_num_layers (`int`, *optional*, defaults to 3):
Number of convolutional layers to use in each depth-separable block.
duration_predictor_flow_bins (`int`, *optional*, defaults to 10):
Number of channels to map using the unonstrained rational spline in the duration predictor... | 3,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py |
duration_predictor_filter_channels (`int`, *optional*, defaults to 256):
Number of channels for the convolution layers used in the duration predictor model.
prior_encoder_num_flows (`int`, *optional*, defaults to 4):
Number of flow stages used by the prior encoder flow model.
pri... | 3,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py |
The dropout ratio for the WaveNet layers.
speaking_rate (`float`, *optional*, defaults to 1.0):
Speaking rate. Larger values give faster synthesised speech.
noise_scale (`float`, *optional*, defaults to 0.667):
How random the speech prediction is. Larger values create more variat... | 3,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py |
Example:
```python
>>> from transformers import VitsModel, VitsConfig
>>> # Initializing a "facebook/mms-tts-eng" style configuration
>>> configuration = VitsConfig()
>>> # Initializing a model (with random weights) from the "facebook/mms-tts-eng" style configuration
>>> model = VitsModel(con... | 3,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py |
def __init__(
self,
vocab_size=38,
hidden_size=192,
num_hidden_layers=6,
num_attention_heads=2,
window_size=4,
use_bias=True,
ffn_dim=768,
layerdrop=0.1,
ffn_kernel_size=3,
flow_size=192,
spectrogram_bins=513,
hidden... | 3,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py |
duration_predictor_kernel_size=3,
duration_predictor_dropout=0.5,
duration_predictor_num_flows=4,
duration_predictor_filter_channels=256,
prior_encoder_num_flows=4,
prior_encoder_num_wavenet_layers=4,
posterior_encoder_num_wavenet_layers=16,
wavenet_kernel_size=5,... | 3,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py |
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.use_stochastic_duration_prediction = use_stochastic_duration_predictio... | 3,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py |
self.duration_predictor_tail_bound = duration_predictor_tail_bound
self.duration_predictor_kernel_size = duration_predictor_kernel_size
self.duration_predictor_dropout = duration_predictor_dropout
self.duration_predictor_num_flows = duration_predictor_num_flows
self.duration_predictor_fi... | 3,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py |
if len(upsample_kernel_sizes) != len(upsample_rates):
raise ValueError(
f"The length of `upsample_kernel_sizes` ({len(upsample_kernel_sizes)}) must match the length of "
f"`upsample_rates` ({len(upsample_rates)})"
)
super().__init__(**kwargs) | 3,242 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/configuration_vits.py |
class VitsTokenizer(PreTrainedTokenizer):
"""
Construct a VITS tokenizer. Also supports MMS-TTS.
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:
vocab_fil... | 3,243 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/tokenization_vits.py |
vocab_files_names = VOCAB_FILES_NAMES
model_input_names = ["input_ids", "attention_mask"]
def __init__(
self,
vocab_file,
pad_token="<pad>",
unk_token="<unk>",
language=None,
add_blank=True,
normalize=True,
phonemize=True,
is_uroman=False,... | 3,243 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/tokenization_vits.py |
@property
def vocab_size(self):
return len(self.encoder)
def get_vocab(self):
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def normalize_text(self, input_string):
"""Lowercase the inp... | 3,243 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/tokenization_vits.py |
def _preprocess_char(self, text):
"""Special treatment of characters in certain languages"""
if self.language == "ron":
text = text.replace("ț", "ţ")
return text
def prepare_for_tokenization(
self, text: str, is_split_into_words: bool = False, normalize: Optional[bool] =... | 3,243 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/tokenization_vits.py |
Args:
text (`str`):
The text to prepare.
is_split_into_words (`bool`, *optional*, defaults to `False`):
Whether or not the input is already pre-tokenized (e.g., split into words). If set to `True`, the
tokenizer assumes the input is already split i... | 3,243 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/tokenization_vits.py |
Returns:
`Tuple[str, Dict[str, Any]]`: The prepared text and the unused kwargs.
"""
normalize = normalize if normalize is not None else self.normalize
if normalize:
# normalise for casing
text = self.normalize_text(text)
filtered_text = self._preproc... | 3,243 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/tokenization_vits.py |
if has_non_roman_characters(filtered_text) and self.is_uroman:
if not is_uroman_available():
logger.warning(
"Text to the tokenizer contains non-Roman characters. To apply the `uroman` pre-processing "
"step automatically, ensure the `uroman` Romanizer... | 3,243 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/tokenization_vits.py |
filtered_text = phonemizer.phonemize(
filtered_text,
language="en-us",
backend="espeak",
strip=True,
preserve_punctuation=True,
with_stress=True,
)
filtered_text = re.sub(r"\s+", " ", filtered_text)
... | 3,243 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/tokenization_vits.py |
def convert_tokens_to_string(self, tokens: List[str]) -> str:
if self.add_blank and len(tokens) > 1:
tokens = tokens[1::2]
return "".join(tokens)
def _convert_token_to_id(self, token):
"""Converts a token (str) in an id using the vocab."""
return self.encoder.get(token, ... | 3,243 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/tokenization_vits.py |
with open(vocab_file, "w", encoding="utf-8") as f:
f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")
return (vocab_file,) | 3,243 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/tokenization_vits.py |
class VitsModelOutput(ModelOutput):
"""
Describes the outputs for the VITS model, with potential hidden states and attentions. | 3,244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
Args:
waveform (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
The final audio waveform predicted by the model.
sequence_lengths (`torch.FloatTensor` of shape `(batch_size,)`):
The length in samples of each element in the `waveform` batch.
spectrogram (`t... | 3,244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shap... | 3,244 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsTextEncoderOutput(ModelOutput):
"""
Describes the outputs for the VITS text encoder model, with potential hidden states and attentions. | 3,245 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
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.
prior_means (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
The predicted mean... | 3,245 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `torch.FloatTensor` (one for each layer) of shap... | 3,245 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsWaveNet(torch.nn.Module):
def __init__(self, config: VitsConfig, num_layers: int):
super().__init__()
self.hidden_size = config.hidden_size
self.num_layers = num_layers
self.in_layers = torch.nn.ModuleList()
self.res_skip_layers = torch.nn.ModuleList()
self... | 3,246 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
for i in range(num_layers):
dilation = config.wavenet_dilation_rate**i
padding = (config.wavenet_kernel_size * dilation - dilation) // 2
in_layer = torch.nn.Conv1d(
in_channels=config.hidden_size,
out_channels=2 * config.hidden_size,
ke... | 3,246 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
def forward(self, inputs, padding_mask, global_conditioning=None):
outputs = torch.zeros_like(inputs)
num_channels_tensor = torch.IntTensor([self.hidden_size])
if global_conditioning is not None:
global_conditioning = self.cond_layer(global_conditioning)
for i in range(self... | 3,246 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
res_skip_acts = self.res_skip_layers[i](acts)
if i < self.num_layers - 1:
res_acts = res_skip_acts[:, : self.hidden_size, :]
inputs = (inputs + res_acts) * padding_mask
outputs = outputs + res_skip_acts[:, self.hidden_size :, :]
else:
... | 3,246 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsPosteriorEncoder(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.out_channels = config.flow_size
self.conv_pre = nn.Conv1d(config.spectrogram_bins, config.hidden_size, 1)
self.wavenet = VitsWaveNet(config, num_layers=config.posterior_encoder_num... | 3,247 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class HifiGanResidualBlock(nn.Module):
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5), leaky_relu_slope=0.1):
super().__init__()
self.leaky_relu_slope = leaky_relu_slope
self.convs1 = nn.ModuleList(
[
nn.Conv1d(
channels,
... | 3,248 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
def get_padding(self, kernel_size, dilation=1):
return (kernel_size * dilation - dilation) // 2
def apply_weight_norm(self):
weight_norm = nn.utils.weight_norm
if hasattr(nn.utils.parametrizations, "weight_norm"):
weight_norm = nn.utils.parametrizations.weight_norm
for ... | 3,248 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
def forward(self, hidden_states):
for conv1, conv2 in zip(self.convs1, self.convs2):
residual = hidden_states
hidden_states = nn.functional.leaky_relu(hidden_states, self.leaky_relu_slope)
hidden_states = conv1(hidden_states)
hidden_states = nn.functional.leaky_re... | 3,248 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsHifiGan(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.config = config
self.num_kernels = len(config.resblock_kernel_sizes)
self.num_upsamples = len(config.upsample_rates)
self.conv_pre = nn.Conv1d(
config.flow_size,
... | 3,249 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
self.resblocks = nn.ModuleList()
for i in range(len(self.upsampler)):
channels = config.upsample_initial_channel // (2 ** (i + 1))
for kernel_size, dilation in zip(config.resblock_kernel_sizes, config.resblock_dilation_sizes):
self.resblocks.append(HifiGanResidualBlock(ch... | 3,249 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
def remove_weight_norm(self):
for layer in self.upsampler:
nn.utils.remove_weight_norm(layer)
for layer in self.resblocks:
layer.remove_weight_norm()
def forward(
self, spectrogram: torch.FloatTensor, global_conditioning: Optional[torch.FloatTensor] = None
) -> t... | 3,249 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
if global_conditioning is not None:
hidden_states = hidden_states + self.cond(global_conditioning)
for i in range(self.num_upsamples):
hidden_states = nn.functional.leaky_relu(hidden_states, self.config.leaky_relu_slope)
hidden_states = self.upsampler[i](hidden_states)
... | 3,249 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsResidualCouplingLayer(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.half_channels = config.flow_size // 2
self.conv_pre = nn.Conv1d(self.half_channels, config.hidden_size, 1)
self.wavenet = VitsWaveNet(config, num_layers=config.prior_encoder_n... | 3,250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
if not reverse:
second_half = mean + second_half * torch.exp(log_stddev) * padding_mask
outputs = torch.cat([first_half, second_half], dim=1)
log_determinant = torch.sum(log_stddev, [1, 2])
return outputs, log_determinant
else:
second_half = (second_ha... | 3,250 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsResidualCouplingBlock(nn.Module):
def __init__(self, config: VitsConfig):
super().__init__()
self.flows = nn.ModuleList()
for _ in range(config.prior_encoder_num_flows):
self.flows.append(VitsResidualCouplingLayer(config))
def forward(self, inputs, padding_mask, gl... | 3,251 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
class VitsDilatedDepthSeparableConv(nn.Module):
def __init__(self, config: VitsConfig, dropout_rate=0.0):
super().__init__()
kernel_size = config.duration_predictor_kernel_size
channels = config.hidden_size
self.num_layers = config.depth_separable_num_layers | 3,252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
self.dropout = nn.Dropout(dropout_rate)
self.convs_dilated = nn.ModuleList()
self.convs_pointwise = nn.ModuleList()
self.norms_1 = nn.ModuleList()
self.norms_2 = nn.ModuleList()
for i in range(self.num_layers):
dilation = kernel_size**i
padding = (kernel_s... | 3,252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
def forward(self, inputs, padding_mask, global_conditioning=None):
if global_conditioning is not None:
inputs = inputs + global_conditioning
for i in range(self.num_layers):
hidden_states = self.convs_dilated[i](inputs * padding_mask)
hidden_states = self.norms_1[i](... | 3,252 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/vits/modeling_vits.py |
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