id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
10,858 | from typing import List, Optional, Union
import numpy as np
from PIL import Image
from transformers.image_utils import PILImageResampling
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
from ...utils import TensorType, ... | Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes. |
10,859 | import math
import warnings
from typing import Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...modeling_outputs import (
BaseM... | Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on CPU as part of the preprocessing during training. Args: shape: The shape for which to comp... |
10,860 | import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN, gelu
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseM... | Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: torch.Tensor x: Returns: torch.Tensor |
10,861 | import collections.abc
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
B... | Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discu... |
10,862 | import argparse
import os
from functools import reduce
import fairseq
import torch
from datasets import load_dataset
from transformers import Wav2Vec2Processor, logging
from transformers.models.data2vec.configuration_data2vec_audio import Data2VecAudioConfig
from transformers.models.data2vec.data2vec_audio import Data2... | Copy/paste/tweak model's weights to transformers design. |
10,863 | import argparse
import os
import pathlib
import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import Data2VecTextConfig, Data2VecTextForMaskedLM, Data2VecTextForSequenceClassification
from transformers.models.bert.modeling_bert import (
... | Copy/paste/tweak data2vec's weights to our BERT structure. |
10,864 | import argparse
import json
import torch
from PIL import Image
from huggingface_hub import hf_hub_download
from timm.models import create_model
from transformers import (
BeitFeatureExtractor,
Data2VecVisionConfig,
Data2VecVisionForImageClassification,
Data2VecVisionModel,
)
def create_rename_keys(conf... | null |
10,865 | import argparse
import json
import torch
from PIL import Image
from huggingface_hub import hf_hub_download
from timm.models import create_model
from transformers import (
BeitFeatureExtractor,
Data2VecVisionConfig,
Data2VecVisionForImageClassification,
Data2VecVisionModel,
)
def read_in_q_k_v(state_dic... | null |
10,866 | import argparse
import json
import torch
from PIL import Image
from huggingface_hub import hf_hub_download
from timm.models import create_model
from transformers import (
BeitFeatureExtractor,
Data2VecVisionConfig,
Data2VecVisionForImageClassification,
Data2VecVisionModel,
)
def get_args():
parser ... | null |
10,867 | import argparse
import json
import torch
from PIL import Image
from huggingface_hub import hf_hub_download
from timm.models import create_model
from transformers import (
BeitFeatureExtractor,
Data2VecVisionConfig,
Data2VecVisionForImageClassification,
Data2VecVisionModel,
)
def load_beit_model(args, i... | null |
10,868 | import math
from typing import Dict, Optional, Sequence, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFMaskedLMOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTok... | Build relative position according to the query and key We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key \\(P_k\\) is range from (0, key_size), The relative positions from query to key is \\(R_{q \\rightarrow k} = P_q - P_k\\) Args: query_size (int): the l... |
10,869 | import math
from typing import Dict, Optional, Sequence, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFMaskedLMOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTok... | null |
10,870 | import math
from typing import Dict, Optional, Sequence, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFMaskedLMOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTok... | null |
10,871 | import math
from typing import Dict, Optional, Sequence, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFMaskedLMOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTok... | null |
10,872 | import math
from typing import Dict, Optional, Sequence, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFMaskedLMOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTok... | null |
10,873 | import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `bytes_to_unicode` function. Write a Python func... | Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like... |
10,874 | import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `get_pairs` function. Write a Python function `d... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
10,875 | from collections.abc import Sequence
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
MaskedLMOutput,
... | null |
10,876 | from collections.abc import Sequence
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
MaskedLMOutput,
... | Build relative position according to the query and key We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key \\(P_k\\) is range from (0, key_size), The relative positions from query to key is \\(R_{q \\rightarrow k} = P_q - P_k\\) Args: query_size (int): the l... |
10,877 | from collections.abc import Sequence
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
MaskedLMOutput,
... | null |
10,878 | from collections.abc import Sequence
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
MaskedLMOutput,
... | null |
10,879 | from collections.abc import Sequence
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
MaskedLMOutput,
... | null |
10,880 | import math
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions
from ...modeling_util... | null |
10,881 | from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import PreTrained... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
10,882 | from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import PreTrained... | null |
10,883 | from typing import List, Optional, Union
import numpy as np
from PIL import Image
from transformers.image_utils import PILImageResampling
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
from ...utils import TensorType, ... | Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format (left, top, right, bottom). |
10,884 | import argparse
import collections
import torch
import torch.nn as nn
import jax
import jax.numpy as jnp
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository
from transformers import (
CLIPTokenizer,
OwlViTConfig,
OwlViTFeatureExtractor,
OwlViTForObjec... | null |
10,885 | import argparse
import collections
import torch
import torch.nn as nn
import jax
import jax.numpy as jnp
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository
from transformers import (
CLIPTokenizer,
OwlViTConfig,
OwlViTFeatureExtractor,
OwlViTForObjec... | null |
10,886 | import argparse
import collections
import torch
import torch.nn as nn
import jax
import jax.numpy as jnp
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository
from transformers import (
CLIPTokenizer,
OwlViTConfig,
OwlViTFeatureExtractor,
OwlViTForObjec... | Copy/paste/tweak model's weights to transformers design. |
10,887 | import argparse
import json
from pathlib import Path
import torch
from PIL import Image
import requests
import timm
from huggingface_hub import hf_hub_download
from transformers import DeiTConfig, DeiTFeatureExtractor, DeiTForImageClassificationWithTeacher
from transformers.utils import logging
def create_rename_keys(c... | Copy/paste/tweak model's weights to our DeiT structure. |
10,888 | import argparse
import json
from pathlib import Path
import torch
from PIL import Image
import requests
import timm
from huggingface_hub import hf_hub_download
from transformers import DeiTFeatureExtractor, ViTConfig, ViTFeatureExtractor, ViTForImageClassification, ViTModel
from transformers.utils import logging
def cr... | Copy/paste/tweak model's weights to our ViT structure. |
10,889 | import argparse
import json
from pathlib import Path
import torch
from PIL import Image
import requests
from huggingface_hub import hf_hub_download
from transformers import ViTConfig, ViTFeatureExtractor, ViTForImageClassification, ViTModel
from transformers.utils import logging
def create_rename_keys(config, base_mode... | Copy/paste/tweak model's weights to our ViT structure. |
10,890 | import copy
from typing import Any, Callable, List, Optional, Tuple
import numpy as np
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
f... | Shift input ids one token to the right. |
10,891 | import copy
from typing import Any, Callable, List, Optional, Tuple
import numpy as np
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
f... | Prepare attention mask to be applied for a local attention. |
10,892 | import copy
from typing import Any, Callable, List, Optional, Tuple
import numpy as np
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
f... | Create the relative position tensor for local -> global attention. |
10,893 | import copy
from typing import Any, Callable, List, Optional, Tuple
import numpy as np
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
f... | Compute individual block aggregates by summing over individual blocks. |
10,894 | import argparse
from t5x import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeq2SeqLM
def convert_t5x_checkpoint_to_flax(t5x_checkpoint_path, config_name, flax_dump_folder_path):
config = AutoConfig.from_pretrained(config_name)
flax_model = FlaxAutoModelForSeq2SeqLM.from_config(config=conf... | null |
10,895 | import copy
import math
import warnings
from typing import Any, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from torch.utils.checkpoint import checkpoint
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputW... | Prepare attention mask to be applied for a local attention. |
10,896 | import copy
import math
import warnings
from typing import Any, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from torch.utils.checkpoint import checkpoint
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputW... | Create the relative position tensor for local -> global attention. |
10,897 | import copy
import math
import warnings
from typing import Any, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from torch.utils.checkpoint import checkpoint
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputW... | Compute individual block aggregates by summing over individual blocks. |
10,898 | import math
import random
from functools import partial
from typing import Callable, Optional, Tuple
import numpy as np
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen.attenti... | Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not have a single `decoder_start_token_id` in contrast to other Bart-like models. |
10,899 | import random
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
from ...modeling_tf_utils import... | Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not have a single `decoder_start_token_id` in contrast to other Bart-like models. |
10,900 | import random
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
from ...modeling_tf_utils import... | Make causal mask used for bi-directional self-attention. |
10,901 | import random
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
from ...modeling_tf_utils import... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
10,902 | import copy
import math
import random
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOu... | Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not have a single `decoder_start_token_id` in contrast to other Bart-like models. |
10,903 | import copy
import math
import random
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOu... | Make causal mask used for bi-directional self-attention. |
10,904 | import copy
import math
import random
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOu... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
10,905 | import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def remove_ignore_keys_(state_dict):
ignore_keys = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"_float_tensor",
... | null |
10,906 | import warnings
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_utils import PoolerAnswerClass, PoolerEndLogits, PoolerStartLogits, Pre... | Load tf checkpoints in a pytorch model |
10,907 | import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
GLUE_TASKS_NUM_LABELS = {
"cola": 2,
"mnli... | null |
10,908 | import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def convert_checkpoint_helper(max_position_embeddings, orig_state_dict):
for key in orig_state_dict.copy().keys():
val = orig_state_dict.pop(key)
if ("pooler" in key) or ("sen_class" in key):
continue
... | null |
10,909 | import math
import os
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithCrossAttentions,
MaskedLMOutput... | null |
10,910 | import math
import os
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithCrossAttentions,
MaskedLMOutput... | null |
10,911 | import math
import os
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithCrossAttentions,
MaskedLMOutput... | null |
10,912 | import math
import os
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithCrossAttentions,
MaskedLMOutput... | null |
10,913 | from typing import List, Optional, Union
import numpy as np
from PIL import Image
from transformers.image_utils import PILImageResampling
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, ImageFeatureExtractionMixin, is... | Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes. |
10,914 | import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import (
BatchEncoding,
EncodedInput,
PreTokenizedInput,
TextInput,
TextInp... | Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like... |
10,915 | import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import (
BatchEncoding,
EncodedInput,
PreTokenizedInput,
TextInput,
TextInp... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
10,916 | import argparse
import numpy as np
import torch
import gdown
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEFeatureExtractor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)
def get_xclip_con... | null |
10,917 | from copy import copy
from dataclasses import dataclass
from typing import Any, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import PreTrainedMod... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
10,918 | from copy import copy
from dataclasses import dataclass
from typing import Any, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import PreTrainedMod... | null |
10,919 | from copy import copy
from dataclasses import dataclass
from typing import Any, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...modeling_utils import PreTrainedMod... | Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discu... |
10,920 | import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.cuda.amp import autocast
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutputWithPastAndCrossAttentions
from ...modeli... | Load tf checkpoints in a pytorch model |
10,921 | import argparse
import collections
from pathlib import Path
import torch
from torch.serialization import default_restore_location
from .transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader
CheckpointState = collections.namedtuple(
"CheckpointState", ["model_dict", "optimizer_d... | null |
10,922 | import argparse
import collections
from pathlib import Path
import torch
from torch.serialization import default_restore_location
from .transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader
class DPRState:
def __init__(self, src_file: Path):
self.src_file = src_file
... | null |
10,923 | import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBart50Tokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
Wav2Vec2Config,
Wav2Vec2FeatureExtractor,
Wav2Vec2Model,
logging,
)
def make_linear_fro... | null |
10,924 | import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBart50Tokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
Wav2Vec2Config,
Wav2Vec2FeatureExtractor,
Wav2Vec2Model,
logging,
)
logger = logging.get... | Copy/paste/tweak model's weights to transformers design. |
10,925 | from typing import Optional
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ...configuration_utils import PretrainedConfig
from ...modeling_outputs import BaseModelOutput, Seq2SeqLMOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docs... | Shift input ids one token to the right. |
10,926 | import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
Speech2Text2Config,
Speech2Text2ForCausalLM,
Speech2Text2Tokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
Wav2Vec2Config,
Wav2Vec2FeatureExtractor,
Wav2Vec2M... | null |
10,927 | import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
Speech2Text2Config,
Speech2Text2ForCausalLM,
Speech2Text2Tokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
Wav2Vec2Config,
Wav2Vec2FeatureExtractor,
Wav2Vec2M... | Copy/paste/tweak model's weights to transformers design. |
10,928 | import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN, gelu
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseM... | Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: torch.Tensor x: Returns: torch.Tensor |
10,929 | import argparse
import torch
from transformers import HubertConfig, HubertForSequenceClassification, Wav2Vec2FeatureExtractor, logging
SUPPORTED_MODELS = ["UtteranceLevel"]
The provided code snippet includes necessary dependencies for implementing the `convert_s3prl_checkpoint` function. Write a Python function `def c... | Copy/paste/tweak model's weights to transformers design. |
10,930 | import warnings
from typing import Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers.deepspeed import is_deepspeed_zero3_enabled
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutp... | Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on CPU as part of the preprocessing during training. Args: shape: The shape for which to comp... |
10,931 | import argparse
import torch
from s3prl.hub import distilhubert
from transformers import HubertConfig, HubertModel, Wav2Vec2FeatureExtractor, logging
def recursively_load_weights(fairseq_model, hf_model):
unused_weights = []
fairseq_dict = fairseq_model.state_dict()
feature_extractor = hf_model.feature_extr... | Copy/paste/tweak model's weights to transformers design. |
10,932 | import inspect
import warnings
from collections.abc import Mapping
from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput
from ...modeling_tf_utils import TFPre... | Process the input of each TensorFlow model including the booleans. In case of a list of symbolic inputs, each input has to be named accordingly to the parameters name, i.e. `input_values = tf.keras.Input(shape=(128,), dtype='float32', name="input_values")` otherwise the order of the tensors will not be guaranteed durin... |
10,933 | import inspect
import warnings
from collections.abc import Mapping
from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput
from ...modeling_tf_utils import TFPre... | Computes random mask spans for a given shape Args: shape: the shape for which to compute masks. should be of size 2 where first element is batch size and 2nd is timesteps attention_mask: optional padding mask of the same size as shape, which will prevent masking padded elements mask_prob: probability for each token to ... |
10,934 | import inspect
import warnings
from collections.abc import Mapping
from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput
from ...modeling_tf_utils import TFPre... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
10,935 | import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
Wav2Vec2CTCTokenizer,
Wav2Vec2FeatureExtractor,
Wav2Vec2Processor,
logging,
)
logger = logging.get_logger(__name__)
def re... | Copy/paste/tweak model's weights to transformers design. |
10,936 | import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
Wav2Vec2Config,
Wav2Vec2CTCTokenizer,
Wav2Vec2FeatureExtractor,
Wav2Vec2ForCTC,
Wav2Vec2ForPreTraining,
Wav2Vec2Processor,
logging,
)
logger = logging.get_logger(_... | Copy/paste/tweak model's weights to transformers design. |
10,937 | import inspect
import warnings
from collections.abc import Mapping
from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput
from... | Process the input of each TensorFlow model including the booleans. In case of a list of symbolic inputs, each input has to be named accordingly to the parameters name, i.e. `input_values = tf.keras.Input(shape=(128,), dtype='float32', name="input_values")` otherwise the order of the tensors will not be guaranteed durin... |
10,938 | import inspect
import warnings
from collections.abc import Mapping
from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput
from... | Computes random mask spans for a given shape Args: shape: the shape for which to compute masks. should be of size 2 where first element is batch size and 2nd is timesteps attention_mask: optional padding mask of the same size as shape, which will prevent masking padded elements mask_prob: probability for each token to ... |
10,939 | import inspect
import warnings
from collections.abc import Mapping
from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFCausalLMOutput
from... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
10,940 | import argparse
import torch
from transformers import (
Wav2Vec2Config,
Wav2Vec2FeatureExtractor,
Wav2Vec2ForAudioFrameClassification,
Wav2Vec2ForSequenceClassification,
Wav2Vec2ForXVector,
logging,
)
def convert_classification(base_model_name, hf_config, downstream_dict):
model = Wav2Vec2Fo... | Copy/paste/tweak model's weights to transformers design. |
10,941 | import math
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...m... | Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on CPU as part of the preprocessing during training. Args: shape: The shape for which to comp... |
10,942 | import math
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...m... | Sample `num_negatives` vectors from feature vectors. |
10,943 | from functools import partial
from typing import Optional, Tuple, Union
import numpy as np
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import f... | Computes random mask spans for a given shape. Used to implement [SpecAugment: A Simple Data Augmentation Method for ASR](https://arxiv.org/abs/1904.08779). Note that this method is not optimized to run on TPU and should be run on CPU as part of the preprocessing during training. Args: shape: the shape for which to comp... |
10,944 | from functools import partial
from typing import Optional, Tuple, Union
import numpy as np
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import f... | Sample `num_negatives` vectors from feature vectors. |
10,945 | from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
def get_default_vocab_list():
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
... | null |
10,946 | import argparse
import pathlib
from pathlib import Path
from tempfile import TemporaryDirectory
import torch
import esm as esm_module
from esm.esmfold.v1.pretrained import esmfold_v1
from transformers.models.esm.configuration_esm import EsmConfig, EsmFoldConfig
from transformers.models.esm.modeling_esm import (
Esm... | Copy/paste/tweak esm's weights to our BERT structure. |
10,947 | import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
def load_vocab_file(vocab_file):
with open(vocab_file, "r") as f:
lines = f.read().splitlines()
return [l.strip() f... | null |
10,948 | from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from tensorflow.keras.activations import gelu
from tensorflow.keras.layers import Dense, Dropout, Embedding, Layer, LayerNormalization
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_... | null |
10,949 | from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from tensorflow.keras.activations import gelu
from tensorflow.keras.layers import Dense, Dropout, Embedding, Layer, LayerNormalization
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_... | Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: tf.Tensor x: Returns: tf.Tensor |
10,950 | import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling... | null |
10,951 | import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling... | This is the gelu implementation from the original ESM repo. Using F.gelu yields subtly wrong results. |
10,952 | import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling... | Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols are ignored. This is modified from fairseq's `utils.make_positions`. Args: x: torch.Tensor x: Returns: torch.Tensor |
10,953 | import math
import sys
from dataclasses import dataclass
from functools import partial
from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from torch.nn import LayerNorm
from ...deepspeed import is_deepspeed_available
from ...modeling_outputs i... | null |
10,954 | import math
import sys
from dataclasses import dataclass
from functools import partial
from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from torch.nn import LayerNorm
from ...deepspeed import is_deepspeed_available
from ...modeling_outputs i... | Takes a list of tensors with the following dimensions: [(d_11, ..., d_1K), (d_21, ..., d_2K), ..., (d_N1, ..., d_NK)] and stack + pads them into a single tensor of: (N, max_i=1,N { d_i1 }, ..., max_i=1,N {diK}) |
10,955 | import math
import sys
from dataclasses import dataclass
from functools import partial
from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from torch.nn import LayerNorm
from ...deepspeed import is_deepspeed_available
from ...modeling_outputs i... | null |
10,956 | import math
import sys
from dataclasses import dataclass
from functools import partial
from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from torch.nn import LayerNorm
from ...deepspeed import is_deepspeed_available
from ...modeling_outputs i... | null |
10,957 | import math
import sys
from dataclasses import dataclass
from functools import partial
from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from torch.nn import LayerNorm
from ...deepspeed import is_deepspeed_available
from ...modeling_outputs i... | null |
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