id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
11,279 | 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... | Compares two values and returns their relation or None. |
11,280 | 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... | Adds numeric value spans to a question. |
11,281 | 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... | Parses text in table column-wise and adds the consolidated values. Consolidation refers to finding values with a common types (date or number) Args: table: Table to annotate. min_consolidation_fraction: Fraction of cells in a column that need to have consolidated value. debug_info: Additional information used for loggi... |
11,282 | import enum
import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple
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, Base... | Load tf checkpoints in a PyTorch model. This is an adaptation from load_tf_weights_in_bert - add cell selection and aggregation heads - take into account additional token type embedding layers |
11,283 | import enum
import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple
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, Base... | Computes the minimum over segments. This operations computes the minimum over segments, with support for: - Batching using the first dimensions [B1, B2, ..., Bn]. Each element in a batch can have different indices. - Vectorization using the last dimension [V1, V2, ...]. If they are present, the output will be an elemen... |
11,284 | import enum
import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple
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, Base... | Computes the column logits. Args: sequence_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Also known as last_hidden_state. Sequence of hidden-states at the output of the last layer of the model. column_output_weights (`torch.FloatTensor` of shape `(hidden_size)`): Weights of the lin... |
11,285 | import enum
import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple
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, Base... | 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 (`torch.FloatTensor` of shape ... |
11,286 | import enum
import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple
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, Base... | Computes logits per token Args: sequence_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Also known as last_hidden_state. Sequence of hidden-states at the output of the last layer of the model. temperature (`float`): Temperature for the Bernoulli distribution. output_weights (`torch.... |
11,287 | import enum
import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple
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, Base... | 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... |
11,288 | import enum
import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple
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, Base... | Calculates the aggregation loss per example. Args: logits_aggregation (`torch.FloatTensor` of shape `(batch_size, num_aggregation_labels)`): Logits per aggregation operation. aggregate_mask (`torch.FloatTensor` of shape `(batch_size, )`): A mask set to 1 for examples that should use aggregation functions. aggregation_l... |
11,289 | import enum
import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple
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, Base... | Calculates the regression loss per example. Args: answer (`torch.FloatTensor` of shape `(batch_size,)`): Answer for every example in the batch. Nan if there is no scalar answer. aggregate_mask (`torch.FloatTensor` of shape `(batch_size,)`): A mask set to 1 for examples that should use aggregation functions. dist_per_ce... |
11,290 | import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
def convert_tf_checkpoint_to_pytorch(
task, reset_position_... | null |
11,291 | import copy
import math
import random
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPas... | Shift input ids one token to the right. |
11,292 | import copy
import math
import random
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPas... | Make causal mask used for bi-directional self-attention. |
11,293 | import copy
import math
import random
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPas... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
11,294 | import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
def convert_bigbird_pegasus(tf_weights: dict, config_update: dict) -> BigBirdPegasusForConditionalGeneration:
cfg = BigBirdPegasusC... | null |
11,297 | import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
def load_entity_vocab(entity_vocab_path):
entity_vocab = {}
with open(entity_vocab_path, "r", encoding="utf-8") as f:
... | null |
11,298 | import math
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, gelu
from ...modeling_outputs import BaseModelOutput, BaseModelOutp... | 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 |
11,299 | 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 (
TFBaseModelOutput,
TFMaskedLMOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTokenClassifierOutput,
)
... | 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... |
11,300 | 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 (
TFBaseModelOutput,
TFMaskedLMOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTokenClassifierOutput,
)
... | null |
11,301 | 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 (
TFBaseModelOutput,
TFMaskedLMOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTokenClassifierOutput,
)
... | null |
11,302 | 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 (
TFBaseModelOutput,
TFMaskedLMOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTokenClassifierOutput,
)
... | null |
11,303 | 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 (
TFBaseModelOutput,
TFMaskedLMOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTokenClassifierOutput,
)
... | null |
11,304 | import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as sp
from ...tokenization_utils import PreTrainedTokenizer
The provided code snippet includes necessary dependencies for implementing the `_is_whitespace` function. Write a Python function `def _is_whitespace(char)` ... | Checks whether `chars` is a whitespace character. |
11,305 | import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as sp
from ...tokenization_utils import PreTrainedTokenizer
The provided code snippet includes necessary dependencies for implementing the `_is_control` function. Write a Python function `def _is_control(char)` to sol... | Checks whether `chars` is a control character. |
11,306 | import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as sp
from ...tokenization_utils import PreTrainedTokenizer
The provided code snippet includes necessary dependencies for implementing the `_is_punctuation` function. Write a Python function `def _is_punctuation(char)... | Checks whether `chars` is a punctuation character. |
11,307 | import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as sp
from ...tokenization_utils import PreTrainedTokenizer
The provided code snippet includes necessary dependencies for implementing the `convert_to_unicode` function. Write a Python function `def convert_to_unicode... | Converts `text` to Unicode (if it's not already), assuming utf-8 input. |
11,308 | 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, LayerNorm, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
MaskedL... | null |
11,309 | 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, LayerNorm, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
MaskedL... | 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... |
11,310 | 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, LayerNorm, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
MaskedL... | null |
11,311 | 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, LayerNorm, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
MaskedL... | null |
11,312 | 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, LayerNorm, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
MaskedL... | null |
11,313 | import math
import random
from typing import Optional, Tuple
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqModelOutput,
)
from... | Shift input ids one token to the right. |
11,314 | import math
import random
from typing import Optional, Tuple
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqModelOutput,
)
from... | Make causal mask used for bi-directional self-attention. |
11,315 | import math
import random
from typing import Optional, Tuple
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
Seq2SeqLMOutput,
Seq2SeqModelOutput,
)
from... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
11,316 | 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 PreTrainedTokenizer
from ...utils import logging
def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> sentencepiece.Sentenc... | null |
11,317 | 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 PreTrainedTokenizer
from ...utils import logging
def load_json(path: str) -> Union[Dict, List]:
with open(path, "r") as f:
... | null |
11,318 | 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 PreTrainedTokenizer
from ...utils import logging
def save_json(data, path: str) -> None:
with open(path, "w") as f:
js... | null |
11,319 | import random
from typing import Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation, glu
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
fr... | null |
11,320 | import random
from typing import Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation, glu
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
fr... | Make causal mask used for bi-directional self-attention. |
11,321 | import random
from typing import Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation, glu
from ...modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPastAndCrossAttentions,
TFSeq2SeqLMOutput,
TFSeq2SeqModelOutput,
)
fr... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
11,322 | import argparse
import torch
from torch import nn
from transformers import Speech2TextConfig, Speech2TextForConditionalGeneration
def remove_ignore_keys_(state_dict):
ignore_keys = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"deco... | null |
11,323 | 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 |
11,324 | 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. |
11,325 | 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]`. |
11,326 | 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. |
11,327 | 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 |
11,328 | import copy
import math
import random
from typing import 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,
BaseModelOutputWithP... | Shift input ids one token to the right. |
11,329 | import copy
import math
import random
from typing import 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,
BaseModelOutputWithP... | Make causal mask used for bi-directional self-attention. |
11,330 | import copy
import math
import random
from typing import 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,
BaseModelOutputWithP... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
11,331 | import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
def... | null |
11,332 | import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
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)` ... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
11,333 | 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 ...file_utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_r... | null |
11,334 | 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 ...file_utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
replace_r... | null |
11,335 | import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
def load_original_entity_vocab(entity_vocab_path):
SPECIAL_TOKENS = ["[MASK]", ... | null |
11,336 | import argparse
import torch
from PIL import Image
import requests
from transformers import ViTMAEConfig, ViTMAEFeatureExtractor, ViTMAEForPreTraining
def convert_state_dict(orig_state_dict, config):
for key in orig_state_dict.copy().keys():
val = orig_state_dict.pop(key)
if "qkv" in key:
... | null |
11,337 | import collections.abc
import math
from copy import deepcopy
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 ...file_utils import (
ModelOutput,
add_start_docstrings,
add_start_d... | Create 2D sin/cos positional embeddings. Args: embed_dim (`int`): Embedding dimension. grid_size (`int`): The grid height and width. add_cls_token (`bool`, *optional*, defaults to `False`): Whether or not to add a classification (CLS) token. Returns: (`tf.Tensor` of shape (grid_size*grid_size, embed_dim) or (1+grid_siz... |
11,338 | 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 ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput
from ...model... | Create 2D sin/cos positional embeddings. Args: embed_dim (`int`): Embedding dimension. grid_size (`int`): The grid height and width. add_cls_token (`bool`, *optional*, defaults to `False`): Whether or not to add a classification (CLS) token. Returns: (`torch.FloatTensor` of shape (grid_size*grid_size, embed_dim) or (1+... |
11,339 | import itertools
import warnings
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 (
TFBaseModelOutput,
TFMultipleChoiceModelOutput,
TFQuestionAnsweri... | null |
11,340 | import itertools
import warnings
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 (
TFBaseModelOutput,
TFMultipleChoiceModelOutput,
TFQuestionAnsweri... | Generate hidden states mask, and optionally an attention mask. |
11,341 | import itertools
import math
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import gelu
from ...modeling_outputs import (
BaseModelOutput,
... | null |
11,342 | import itertools
import math
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import gelu
from ...modeling_outputs import (
BaseModelOutput,
... | Generate hidden states mask, and optionally an attention mask. |
11,343 | import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
def convert_xlm_checkpoint_to_pytorch(xlm_checkpoint_path, pytorch_dump_folder_path):
# Load checkpoint
chkpt = torch.lo... | null |
11,344 | import json
import os
import re
import sys
import unicodedata
from typing import List, Optional, Tuple
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... | Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length strings) |
11,345 | import json
import os
import re
import sys
import unicodedata
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `lowercase_and_remove_accent` function. Write a Python... | Lowercase and strips accents from a piece of text based on https://github.com/facebookresearch/XLM/blob/master/tools/lowercase_and_remove_accent.py |
11,346 | import json
import os
import re
import sys
import unicodedata
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `replace_unicode_punct` function. Write a Python funct... | Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/replace-unicode-punctuation.perl |
11,347 | import json
import os
import re
import sys
import unicodedata
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `remove_non_printing_char` function. Write a Python fu... | Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/remove-non-printing-char.perl |
11,348 | import json
import os
import re
import sys
import unicodedata
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `romanian_preprocessing` function. Write a Python func... | Sennrich's WMT16 scripts for Romanian preprocessing, used by model `xlm-mlm-enro-1024` |
11,349 | import json
import os
import unicodedata
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
The provided code snippet includes necessary ... | 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... |
11,350 | import json
import os
import unicodedata
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
The provided code snippet includes necessary ... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
11,351 | import json
import os
import unicodedata
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
def whitespace_clean(text):
text = re.sub... | null |
11,352 | import json
import os
import unicodedata
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
The provided code snippet includes necessary ... | Runs basic whitespace cleaning and splitting on a piece of text. |
11,353 | import argparse
import torch
from clip import load
from transformers import CLIPConfig, CLIPModel
def copy_text_model_and_projection(hf_model, pt_model):
# copy projection
hf_model.text_projection.weight.data = pt_model.text_projection.data.T
# copy text encoder
copy_encoder(hf_model.text_model, pt_mode... | Copy/paste/tweak model's weights to transformers design. |
11,354 | import math
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, TFBaseModelOutputWithPooling
from ...modeling_tf_utils import (
DUMMY_IN... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
11,355 | import math
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, TFBaseModelOutputWithPooling
from ...modeling_tf_utils import (
DUMMY_IN... | null |
11,356 | 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 PreTrainedModel
from ...utils impor... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
11,357 | 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 PreTrainedModel
from ...utils impor... | null |
11,358 | import sys
from collections import namedtuple
from dataclasses import dataclass
from functools import reduce
from operator import mul
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.autograd.function import Function
from torch.nn import BCEWithLogitsLoss, ... | null |
11,359 | import sys
from collections import namedtuple
from dataclasses import dataclass
from functools import reduce
from operator import mul
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.autograd.function import Function
from torch.nn import BCEWithLogitsLoss, ... | null |
11,360 | import sys
from collections import namedtuple
from dataclasses import dataclass
from functools import reduce
from operator import mul
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.autograd.function import Function
from torch.nn import BCEWithLogitsLoss, ... | null |
11,361 | import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
def set_model_weights_in_torch(weights, torch_model, hidden_size):
def convert_trax_checkpoint_to_pytorch(trax_model_pkl_path, confi... | null |
11,362 | import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple
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_pair... | Return set of symbol pairs in a word. word is represented as tuple of symbols (symbols being variable-length strings) |
11,363 | import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `replace_unicode_punct` function. Write a Python function `... | Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/replace-unicode-punctuation.perl |
11,364 | import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `remove_non_printing_char` function. Write a Python functio... | Port of https://github.com/moses-smt/mosesdecoder/blob/master/scripts/tokenizer/remove-non-printing-char.perl |
11,365 | import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fs... | null |
11,366 | import math
import random
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import Tensor, nn
from torch.nn import CrossEntropyLoss, LayerNorm
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...modeling_outputs import (
BaseModelOutput,
... | Prepare masks that ignore padding tokens in the decoder and a causal mask for the decoder if none are provided. This mimics the default behavior in fairseq. To override it pass in masks. Note: this is not called during generation |
11,367 | import math
import random
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import Tensor, nn
from torch.nn import CrossEntropyLoss, LayerNorm
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...modeling_outputs import (
BaseModelOutput,
... | null |
11,368 | import math
import random
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import Tensor, nn
from torch.nn import CrossEntropyLoss, LayerNorm
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...modeling_outputs import (
BaseModelOutput,
... | null |
11,369 | import math
import random
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import Tensor, nn
from torch.nn import CrossEntropyLoss, LayerNorm
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...modeling_outputs import (
BaseModelOutput,
... | null |
11,370 | import math
import random
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import Tensor, nn
from torch.nn import CrossEntropyLoss, LayerNorm
from ...activations import ACT2FN
from ...deepspeed import is_deepspeed_zero3_enabled
from ...modeling_outputs import (
BaseModelOutput,
... | null |
11,371 | import math
import os
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, get_activation
from ...modeling_outputs import (
... | Load tf checkpoints in a pytorch model. |
11,372 | import argparse
import torch
from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path, discriminator_or_generator):
# Initialise PyTorc... | null |
11,373 | from typing import Callable, 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 import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
from flax.li... | null |
11,376 | import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
OLD_KEY = "lm_head.decoder.weight"
NEW_KEY = "lm_head.weight"
def convert_dialogpt_checkpoint(checkpoint_path: str, pytorch_dump_folder_path: str):
d = torch.load(checkpoint_path)
d[NEW_KEY] = d.pop(OLD_KEY)
os.makedirs(pyto... | null |
11,377 | import collections.abc
import math
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, TFBaseModelOutputWithPooling
from ...modeling_tf_util... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
11,378 | import collections.abc
import math
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, TFBaseModelOutputWithPooling
from ...modeling_tf_util... | null |
11,379 | import collections.abc
import math
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, TFBaseModelOutputWithPooling
from ...modeling_tf_util... | null |
11,380 | import collections.abc
import math
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, TFBaseModelOutputWithPooling
from ...modeling_tf_util... | null |
11,381 | import collections.abc
import math
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, TFBaseModelOutputWithPooling
from ...modeling_tf_util... | Args: attentions (`tuple(tf.Tensor)`: tuple of attention maps returned by `TFGroupViTVisionTransformer` hw_shape (`tuple(int)`): height and width of the output attention map Returns: `tf.Tensor`: the attention map of shape [batch_size, groups, height, width] |
11,382 | import argparse
import torch
from PIL import Image
import requests
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def convert_state_dict(orig_state_dict, config):
for key in orig_state_dict.copy().keys():
val = orig_state_dict.pop(key)
if "qkv" in key:
# weights an... | Copy/paste/tweak model's weights to the Transformers design. |
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