id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
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11,173 | import argparse
import json
import pickle
from pathlib import Path
import numpy as np
import torch
from PIL import Image
import haiku as hk
import requests
from huggingface_hub import hf_hub_download
from transformers import (
PerceiverConfig,
PerceiverFeatureExtractor,
PerceiverForImageClassificationConvPr... | Copy/paste/tweak model's weights to our Perceiver structure. |
11,174 | import abc
import math
from dataclasses import dataclass
from functools import reduce
from operator import __add__
from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, Cros... | Builds the position encoding. Args: - out_channels: refers to the number of channels of the position encodings. - project_pos_dim: if specified, will project the position encodings to this dimension. |
11,175 | import abc
import math
from dataclasses import dataclass
from functools import reduce
from operator import __add__
from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, Cros... | Partitions a [B, N, C] tensor into tensors for each modality. Args: modality_sizes dict specifying the size of the modality inputs: input tensor Returns: dict mapping name of modality to its associated tensor. |
11,176 | import abc
import math
from dataclasses import dataclass
from functools import reduce
from operator import __add__
from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, Cros... | Space to depth transform. Rearranges blocks of spatial data, into depth. This function assumes the channels to be first, but will place the channels last after transformation. Based on https://discuss.pytorch.org/t/is-there-any-layer-like-tensorflows-space-to-depth-function/3487/15. |
11,177 | import abc
import math
from dataclasses import dataclass
from functools import reduce
from operator import __add__
from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, Cros... | Generate a Fourier frequency position encoding with linear spacing. Args: pos (`torch.LongTensor` of shape `(batch_size, sequence_length, dim)`): The Tensor containing the position of n points in d dimensional space. num_bands (`int`): The number of frequency bands (K) to use. max_resolution (`Tuple[int]`, *optional*, ... |
11,178 | import abc
import math
from dataclasses import dataclass
from functools import reduce
from operator import __add__
from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, Cros... | Checks or builds spatial position features (x, y, ...). Args: pos (`torch.FloatTensor`): None, or an array of position features. If None, position features are built. Otherwise, their size is checked. index_dims (`List[int]`): An iterable giving the spatial/index size of the data to be featurized. batch_size (`int`): T... |
11,179 | import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import (
ENCODE_K... | 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... |
11,180 | import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import (
ENCODE_K... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
11,181 | 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 transformers.activations import ACT2FN
from transformers.file_utils import (
add_start_docstrings,
add_start_docs... | 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 |
11,182 | import json
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings
from ...tokenization_utils_base import (
ENCODE_KWARGS_DOCSTRING,
BatchEncoding,
Encod... | 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... |
11,183 | import json
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings
from ...tokenization_utils_base import (
ENCODE_KWARGS_DOCSTRING,
BatchEncoding,
Encod... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
11,184 | import copy
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 import partitioning as nn_partitioning
from flax.li... | Shift input ids one token to the right. |
11,185 | import argparse
from transformers import T5Config, T5ForConditionalGeneration, load_tf_weights_in_t5
from transformers.utils import logging
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path):
# Initialise PyTorch model
config = T5Config.from_json_file(config_file)
prin... | null |
11,186 | import copy
import math
import os
import warnings
from typing import 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,
BaseModelOutputWi... | Load tf checkpoints in a pytorch model. |
11,187 | import argparse
from t5x import checkpoints
from transformers import FlaxT5ForConditionalGeneration, T5Config
def convert_t5x_checkpoint_to_flax(t5x_checkpoint_path, config_name, flax_dump_folder_path):
config = T5Config.from_pretrained(config_name)
flax_model = FlaxT5ForConditionalGeneration(config=config)
... | null |
11,189 | import math
import os
import warnings
from typing import Any, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.cuda.amp import autocast
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
... | Load tf checkpoints in a pytorch model |
11,190 | import argparse
import torch
from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
def convert_imagegpt_checkpoint_to_pytorch(imagegpt_checkpoint_path, model_size, pytorch_dump_folder_path):
# Construct configurat... | null |
11,191 | 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, ... | null |
11,192 | import warnings
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast, TFSequenceClassifierOutput
from ...modeling_tf_utils import (
TFCausalLanguageModelingLoss,
TFModelInputType,
TFPreTrai... | null |
11,193 | import warnings
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWithPast, TFSequenceClassifierOutput
from ...modeling_tf_utils import (
TFCausalLanguageModelingLoss,
TFModelInputType,
TFPreTrai... | null |
11,194 | from typing import Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutput
from ...modeling_utils import PreTrainedModel
from ..... | null |
11,195 | from typing import Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutput
from ...modeling_utils import PreTrainedModel
from ..... | null |
11,196 | from typing import Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutput
from ...modeling_utils import PreTrainedModel
from ..... | null |
11,197 | import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `get_pairs` function. Write a Python function `def get_pairs(word)` to solve the ... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
11,198 | import argparse
import json
import numpy as np
import torch
import gdown
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEFeatureExtractor,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
)
def get_videomae_config(model_name):
def convert_state_dict(... | null |
11,199 | import collections.abc
import math
from copy import deepcopy
from dataclasses import dataclass
from typing import Optional, Set, Tuple, Union
import numpy as np
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import AC... | Sinusoid position encoding table |
11,202 | from typing import Callable, List, Optional, Tuple
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 flatten_dict, unflatte... | get pair-wise relative position index for each token inside the window |
11,203 | from typing import Callable, List, Optional, Tuple
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 flatten_dict, unflatte... | null |
11,204 | 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... |
11,205 | import argparse
import json
from pathlib import Path
import torch
from datasets import load_dataset
from PIL import Image
import requests
from huggingface_hub import hf_hub_download
from transformers import (
BeitConfig,
BeitFeatureExtractor,
BeitForImageClassification,
BeitForMaskedImageModeling,
B... | Copy/paste/tweak model's weights to our BEiT structure. |
11,206 | import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
# Initialise PyTorch model
config = BertConfig.from_json_file(bert_... | null |
11,207 | import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
The provided code snippet includes necessary dependencies for implementing the `convert_pytorch_checkpoint_to_tf` function. Write a Python function `def convert_pytorch_checkpoint_to_tf(model: BertModel... | Args: model: BertModel Pytorch model instance to be converted ckpt_dir: Tensorflow model directory model_name: model name Currently supported HF models: - Y BertModel - N BertForMaskedLM - N BertForPreTraining - N BertForMultipleChoice - N BertForNextSentencePrediction - N BertForSequenceClassification - N BertForQuest... |
11,208 | import math
import os
import warnings
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 (
... | Load tf checkpoints in a pytorch model. |
11,209 | import argparse
import os
import re
import tensorflow as tf
import torch
from transformers import BertConfig, BertModel
from transformers.utils import logging
logger = logging.get_logger(__name__)
def load_tf2_weights_in_bert(model, tf_checkpoint_path, config):
tf_path = os.path.abspath(tf_checkpoint_path)
logg... | null |
11,210 | import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
def con... | null |
11,212 | from typing import Optional
import torch
from torch import nn
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from ..auto.configuration_auto import AutoConfig
from ..auto.modeling_auto import AutoModel
fro... | null |
11,213 | import copy
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.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseMode... | Load tf checkpoints in a pytorch model. |
11,214 | import argparse
from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, pytorch_dump_path):
# Initialize PyTorch model
config = CanineConfig()
model = CanineModel(config)
... | null |
11,215 | import json
import os
import re
import unicodedata
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `get_pairs` functio... | Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length strings) |
11,216 | import json
import os
import re
import unicodedata
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `replace_unicode_pu... | Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/replace-unicode-punctuation.perl |
11,217 | import json
import os
import re
import unicodedata
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `remove_non_printin... | Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/remove-non-printing-char.perl |
11,218 | import json
import os
import re
import unicodedata
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `whitespace_tokeniz... | Runs basic whitespace cleaning and splitting on a piece of text. |
11,219 | 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.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax im... | null |
11,220 | import argparse
import torch
from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
# Initialise PyTorch model
config = RoFormerConfig.from_js... | null |
11,223 | import math
import os
from typing import Optional, Tuple, Union
import numpy as np
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 (
BaseModelOutputWithPastAndCrossAtte... | Load tf checkpoints in a pytorch model. |
11,224 | import math
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, DepthEstimatorOutput
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import find_pruneable_he... | 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... |
11,225 | import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from PIL import Image
import requests
from transformers import GLPNConfig, GLPNFeatureExtractor, GLPNForDepthEstimation
from transformers.utils import logging
logger = logging.get_logger(__name__)
def rename_keys(state_dict):
... | Copy/paste/tweak model's weights to our GLPN structure. |
11,226 | import math
import random
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCro... | Shift input ids one token to the right. |
11,227 | import math
import random
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCro... | Make causal mask used for bi-directional self-attention. |
11,228 | import math
import random
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCro... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
11,229 | import math
import random
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCro... | 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`. |
11,230 | import argparse
import torch
from torch import nn
from transformers import M2M100Config, M2M100ForConditionalGeneration
def remove_ignore_keys_(state_dict):
ignore_keys = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output... | null |
11,231 | import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> senten... | null |
11,232 | import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
def load_json(path: str) -> Union[Dict, List]:
with open(path,... | null |
11,233 | import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
def save_json(data, path: str) -> None:
with open(path, "w") a... | null |
11,234 | import copy
import math
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import LayerNorm
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput
from ...modeling_ut... | null |
11,235 | import copy
import math
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import LayerNorm
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput
from ...modeling_ut... | This function computes the bias for the predict stream |
11,236 | import copy
import math
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import LayerNorm
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput
from ...modeling_ut... | This function computes both main and predict relative position buckets. For more detail, see paper. |
11,237 | import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `load_vocab` function. Write a Python function `... | Loads a vocabulary file into a dictionary. |
11,238 | 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... | null |
11,239 | 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. |
11,240 | import argparse
import datetime
import json
import os
import re
from pathlib import Path
from typing import Tuple
from tqdm import tqdm
import yaml
from transformers.models.marian.convert_marian_to_pytorch import (
FRONT_MATTER_TEMPLATE,
convert,
convert_opus_name_to_hf_name,
download_and_unzip,
get... | null |
11,241 | import argparse
import datetime
import json
import os
import re
from pathlib import Path
from typing import Tuple
from tqdm import tqdm
import yaml
from transformers.models.marian.convert_marian_to_pytorch import (
FRONT_MATTER_TEMPLATE,
convert,
convert_opus_name_to_hf_name,
download_and_unzip,
get... | Preservers order |
11,242 | import random
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
from ...model... | null |
11,243 | import random
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
from ...model... | Make causal mask used for bi-directional self-attention. |
11,244 | import random
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
from ...model... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
11,245 | import argparse
import json
import os
import socket
import time
import warnings
from pathlib import Path
from typing import Dict, List, Union
from zipfile import ZipFile
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
from huggingface_hub.hf_api import list_models
from transformers import Mar... | null |
11,246 | import argparse
import json
import os
import socket
import time
import warnings
from pathlib import Path
from typing import Dict, List, Union
from zipfile import ZipFile
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
from huggingface_hub.hf_api import list_models
from transformers import Mar... | null |
11,247 | import argparse
import json
import os
import socket
import time
import warnings
from pathlib import Path
from typing import Dict, List, Union
from zipfile import ZipFile
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
from huggingface_hub.hf_api import list_models
from transformers import Mar... | Find models that can accept src_lang as input and return tgt_lang as output. |
11,248 | import argparse
import json
import os
import socket
import time
import warnings
from pathlib import Path
from typing import Dict, List, Union
from zipfile import ZipFile
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
from huggingface_hub.hf_api import list_models
from transformers import Mar... | null |
11,249 | import argparse
import json
import os
import socket
import time
import warnings
from pathlib import Path
from typing import Dict, List, Union
from zipfile import ZipFile
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
from huggingface_hub.hf_api import list_models
from transformers import Mar... | null |
11,250 | import argparse
import json
import os
import socket
import time
import warnings
from pathlib import Path
from typing import Dict, List, Union
from zipfile import ZipFile
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
from huggingface_hub.hf_api import list_models
from transformers import Mar... | null |
11,251 | import argparse
import json
import os
import socket
import time
import warnings
from pathlib import Path
from typing import Dict, List, Union
from zipfile import ZipFile
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
from huggingface_hub.hf_api import list_models
from transformers import Mar... | Copy the most recent model's readme section from opus, and add metadata. upload command: aws s3 sync model_card_dir s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun |
11,252 | import argparse
import json
import os
import socket
import time
import warnings
from pathlib import Path
from typing import Dict, List, Union
from zipfile import ZipFile
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
from huggingface_hub.hf_api import list_models
from transformers import Mar... | Requires 300GB |
11,253 | import argparse
import json
import os
import socket
import time
import warnings
from pathlib import Path
from typing import Dict, List, Union
from zipfile import ZipFile
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
from huggingface_hub.hf_api import list_models
from transformers import Mar... | null |
11,254 | import argparse
import json
import os
import socket
import time
import warnings
from pathlib import Path
from typing import Dict, List, Union
from zipfile import ZipFile
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
from huggingface_hub.hf_api import list_models
from transformers import Mar... | null |
11,255 | import argparse
import json
import os
import socket
import time
import warnings
from pathlib import Path
from typing import Dict, List, Union
from zipfile import ZipFile
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
from huggingface_hub.hf_api import list_models
from transformers import Mar... | null |
11,256 | import argparse
import json
import os
import socket
import time
import warnings
from pathlib import Path
from typing import Dict, List, Union
from zipfile import ZipFile
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
from huggingface_hub.hf_api import list_models
from transformers import Mar... | null |
11,257 | import argparse
import json
import os
import socket
import time
import warnings
from pathlib import Path
from typing import Dict, List, Union
from zipfile import ZipFile
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
from huggingface_hub.hf_api import list_models
from transformers import Mar... | null |
11,258 | import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
def load_spm(path: str, sp_model_kwargs: Dict[str, Any]... | null |
11,259 | import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
def save_json(data, path: str) -> None:
with open(p... | null |
11,260 | import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
def load_json(path: str) -> Union[Dict, List]:
with... | null |
11,261 | import copy
import math
import random
from typing import Dict, List, 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 ...modeling_outputs import (
BaseModelOutput,
BaseModelOutpu... | Shift input ids one token to the right. |
11,262 | import copy
import math
import random
from typing import Dict, List, 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 ...modeling_outputs import (
BaseModelOutput,
BaseModelOutpu... | Make causal mask used for bi-directional self-attention. |
11,263 | import copy
import math
import random
from typing import Dict, List, 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 ...modeling_outputs import (
BaseModelOutput,
BaseModelOutpu... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
11,264 | import collections
import os
import unicodedata
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...tokenization_utils_base import BatchEncoding
from ...utils import PaddingStrategy, logging
The provided code snippet inclu... | Loads a vocabulary file into a dictionary. |
11,265 | import collections
import os
import unicodedata
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...tokenization_utils_base import BatchEncoding
from ...utils import PaddingStrategy, logging
The provided code snippet inclu... | Runs basic whitespace cleaning and splitting on a piece of text. |
11,266 | import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCr... | Load tf checkpoints in a pytorch model. |
11,267 | import os
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer
from ...utils import logging
def convert_tfrecord_to_np(block_records_path: str, num_block_records: int) -> np.ndarray:
import tensorflow.compat.v1 as tf
blocks_da... | null |
11,268 | import enum
import math
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutputWithPastAndCrossAttentions,
TFBaseModelOutputWithPooling,
... | Computes the minimum over segments. |
11,269 | import enum
import math
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutputWithPastAndCrossAttentions,
TFBaseModelOutputWithPooling,
... | Computes the loss for cell selection constrained to a single column. The loss is a hierarchical log-likelihood. The model first predicts a column and then selects cells within that column (conditioned on the column). Cells outside the selected column are never selected. Args: token_logits (`tf.Tensor` of shape `(batch_... |
11,270 | import enum
import math
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutputWithPastAndCrossAttentions,
TFBaseModelOutputWithPooling,
... | Finds examples where the model should select cells with no aggregation. Returns a mask that determines for which examples should the model select answers directly from the table, without any aggregation function. If the answer is a piece of text the case is unambiguous as aggregation functions only apply to numbers. If... |
11,271 | import enum
import math
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutputWithPastAndCrossAttentions,
TFBaseModelOutputWithPooling,
... | Calculates the aggregation loss per example. Args: logits_aggregation (`tf.Tensor` of shape `(batch_size, num_aggregation_labels)`): Logits per aggregation operation. aggregate_mask (`tf.Tensor` of shape `(batch_size, )`): A mask set to 1 for examples that should use aggregation functions. aggregation_labels (`tf.Tenso... |
11,272 | import enum
import math
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
TFBaseModelOutputWithPastAndCrossAttentions,
TFBaseModelOutputWithPooling,
... | Calculates the regression loss per example. Args: answer (`tf.Tensor` of shape `(batch_size,)`): Answer for every example in the batch. Nan if there is no scalar answer. aggregate_mask (`tf.Tensor` of shape `(batch_size,)`): A mask set to 1 for examples that should use aggregation functions. dist_per_cell (`torch.distr... |
11,273 | import collections
import datetime
import enum
import itertools
import math
import os
import re
import unicodedata
from dataclasses import dataclass
from typing import Callable, Dict, Generator, List, Optional, Text, Tuple, Union
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is... | null |
11,274 | import collections
import datetime
import enum
import itertools
import math
import os
import re
import unicodedata
from dataclasses import dataclass
from typing import Callable, Dict, Generator, List, Optional, Text, Tuple, Union
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is... | Loads a vocabulary file into a dictionary. |
11,275 | import collections
import datetime
import enum
import itertools
import math
import os
import re
import unicodedata
from dataclasses import dataclass
from typing import Callable, Dict, Generator, List, Optional, Text, Tuple, Union
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is... | Runs basic whitespace cleaning and splitting on a piece of text. |
11,276 | import collections
import datetime
import enum
import itertools
import math
import os
import re
import unicodedata
from dataclasses import dataclass
from typing import Callable, Dict, Generator, List, Optional, Text, Tuple, Union
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is... | null |
11,277 | import collections
import datetime
import enum
import itertools
import math
import os
import re
import unicodedata
from dataclasses import dataclass
from typing import Callable, Dict, Generator, List, Optional, Text, Tuple, Union
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is... | Lowercases and strips punctuation. |
11,278 | import collections
import datetime
import enum
import itertools
import math
import os
import re
import unicodedata
from dataclasses import dataclass
from typing import Callable, Dict, Generator, List, Optional, Text, Tuple, Union
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is... | Creates a function that can be used as a sort key or to compare the values. Maps to primitive types and finds the biggest common subset. Consider the values "05/05/2010" and "August 2007". With the corresponding primitive values (2010.,5.,5.) and (2007.,8., None). These values can be compared by year and date so we map... |
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