id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
11,062 | 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
from ...utils import PaddingStrategy, logging
The provided ... | 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... |
11,063 | 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
from ...utils import PaddingStrategy, logging
The provided ... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
11,064 | import argparse
import json
import torch
from PIL import Image
import requests
import timm
from huggingface_hub import hf_hub_download
from transformers import AutoFeatureExtractor, SwinConfig, SwinForImageClassification
def get_swin_config(swin_name):
def convert_state_dict(orig_state_dict, model):
def convert_swin_c... | null |
11,065 | import collections.abc
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
from ...modeling_utils import PreTrainedModel... | Partitions the given input into windows. |
11,066 | import collections.abc
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
from ...modeling_utils import PreTrainedModel... | Merges windows to produce higher resolution features. |
11,067 | import collections.abc
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
from ...modeling_utils import PreTrainedModel... | 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... |
11,068 | import collections.abc
import math
from dataclasses import dataclass
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACT2FN
from ...modeling_tf_utils import (
TFPreTrainedModel,
TFSequenceClassific... | Partitions the given input into windows. |
11,069 | import collections.abc
import math
from dataclasses import dataclass
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACT2FN
from ...modeling_tf_utils import (
TFPreTrainedModel,
TFSequenceClassific... | Merges windows to produce higher resolution features. |
11,070 | import collections.abc
import math
from dataclasses import dataclass
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACT2FN
from ...modeling_tf_utils import (
TFPreTrainedModel,
TFSequenceClassific... | Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). |
11,071 | import collections.abc
import math
from dataclasses import dataclass
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACT2FN
from ...modeling_tf_utils import (
TFPreTrainedModel,
TFSequenceClassific... | From tensorflow addons https://github.com/tensorflow/addons/blob/8cec33fcaaf1cf90aec7bdd55a0fcdbb251ce5c2/tensorflow_addons/utils/keras_utils.py#L71 |
11,072 | import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
def convert_rembert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
# Initialise PyTorch model
config = RemBertConfig.from_json... | null |
11,073 | 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 (
BaseModelOutputWithPastAndCrossAttentions,
BaseMod... | Load tf checkpoints in a pytorch model. |
11,074 | 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 ...modeling_utils import PreTrainedModel
from ...utils import (
ModelOutput,
add_code_sample_docstrings,
... | Load tf checkpoints in a pytorch model |
11,075 | import glob
import os
import pickle
import re
from collections import Counter, OrderedDict
from typing import List, Optional, Tuple
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import (
cached_file,
is_sacremoses_available,
is_torch_available,
logging,
requi... | Splits large comma-separated numbers and floating point values. This is done by replacing commas with ' @,@ ' and dots with ' @.@ '. Args: text_array: An already tokenized text as list. Returns: A list of strings with tokenized numbers. Example: ```python >>> tokenize_numbers(["$", "5,000", "1.73", "m"]) ["$", "5", "@,... |
11,076 | import glob
import os
import pickle
import re
from collections import Counter, OrderedDict
from typing import List, Optional, Tuple
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import (
cached_file,
is_sacremoses_available,
is_torch_available,
logging,
requi... | Inverts the operation of *tokenize_numbers*. This is replacing ' @,@ ' and ' @.@' by ',' and '.'. Args: text: A string where the number should be detokenized. Returns: A detokenized string. Example: ```python >>> detokenize_numbers("$ 5 @,@ 000 1 @.@ 73 m") "$ 5,000 1.73 m" ``` |
11,077 | import glob
import os
import pickle
import re
from collections import Counter, OrderedDict
from typing import List, Optional, Tuple
import numpy as np
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import (
cached_file,
is_sacremoses_available,
is_torch_available,
logging,
requi... | null |
11,078 | import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, V... | null |
11,079 | 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 |
11,080 | import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassific... | Copy/paste/tweak roberta's weights to our BERT structure. |
11,081 | import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def count_parameters(state_dict):
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items())
d... | Copy/paste/tweak model's weights to transformers design. |
11,082 | import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def count_parameters(state_dict):
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum(... | Copy/paste/tweak model's weights to transformers design. |
11,083 | import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `load_vocab_and_emoji` fun... | Loads a vocabulary file and emoji file into a dictionary. |
11,084 | from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...modeling_outp... | null |
11,085 | from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import CrossEntropyLoss
from ...activations import ACT2FN
from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...modeling_outp... | add bias to x, apply dropout and residual connection Args: x (Tensor): main path of output bias (Tensor): None or attn_bias of the last attention layer residual (Optional[Tensor]): residual value prob (float): dropout probability training (bool): whether in training mode or not Returns: Tensor: dropout(x + bias) + resi... |
11,086 | import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import torch
import timm
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitFeatureExtractor, LevitForImageClassificationWithTeacher
from transformers.utils import l... | null |
11,087 | import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, mobilebert_config_file, pytorch_dump_path):
# Initialise PyTorch model
config = MobileB... | null |
11,088 | import math
import os
import warnings
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
BaseModelOutp... | Load tf checkpoints in a pytorch model. |
11,091 | import math
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 gelu
from ...modeling_outputs import (
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWith... | 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: input_ids (`torch.LongTensor`): Indices of input sequence tokens in the vocabulary. Returns: torch.Tensor |
11,092 | import decimal
import numpy as np
import torch
from torch import nn
from torch.autograd import Function
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `get_percentile_min_max` function. Write a Python function `def get_percentile_min_max(input, lower_percent... | Calculate the percentile max and min values in a given tensor Args: input (`torch.Tensor`): The target tensor to calculate percentile max and min. lower_percentile (`float`): If 0.1, means we return the value of the smallest 0.1% value in the tensor as percentile min. upper_percentile (`float`): If 99.9, means we retur... |
11,093 | import decimal
import numpy as np
import torch
from torch import nn
from torch.autograd import Function
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `linear_quantize` function. Write a Python function `def linear_quantize(input, scale, zero_point, inplace=... | Quantize single-precision input tensor to integers with the given scaling factor and zeropoint. Args: input (`torch.Tensor`): Single-precision input tensor to be quantized. scale (`torch.Tensor`): Scaling factor for quantization. zero_pint (`torch.Tensor`): Shift for quantization. inplace (`bool`, *optional*, defaults ... |
11,094 | import decimal
import numpy as np
import torch
from torch import nn
from torch.autograd import Function
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `symmetric_linear_quantization_params` function. Write a Python function `def symmetric_linear_quantization... | Compute the scaling factor with the given quantization range for symmetric quantization. Args: saturation_min (`torch.Tensor`): Lower bound for quantization range. saturation_max (`torch.Tensor`): Upper bound for quantization range. per_channel (`bool`, *optional*, defaults to `False`): Whether to or not use channel-wi... |
11,095 | import decimal
import numpy as np
import torch
from torch import nn
from torch.autograd import Function
from ...utils import logging
The provided code snippet includes necessary dependencies for implementing the `batch_frexp` function. Write a Python function `def batch_frexp(inputs, max_bit=31)` to solve the followin... | Decompose the scaling factor into mantissa and twos exponent. Args: scaling_factor (`torch.Tensor`): Target scaling factor to decompose. Returns: ``Tuple(torch.Tensor, torch.Tensor)`: mantisa and exponent |
11,096 | 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_utils import PreTrainedModel
from ...pytorch_... | Computes global attention mask by putting attention on all tokens before `sep_token_id` if `before_sep_token is True` else after `sep_token_id`. |
11,097 | 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_utils import PreTrainedModel
from ...pytorch_... | 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 |
11,100 | import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class LightningModel(pl.LightningModule):
def __init__(self, model):
def forward(self):
def convert_longformer_qa_checkpoint_to_pytorch(
longformer_mod... | null |
11,101 | import warnings
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_utils import (
TFMaskedLanguageModelingLoss,
TFModelInputType,
TFMultipleChoiceLoss,
TFPreTrainedMode... | Computes global attention mask by putting attention on all tokens before `sep_token_id` if `before_sep_token is True` else after `sep_token_id`. |
11,102 | import argparse
import json
from pathlib import Path
import torch
from PIL import Image
import requests
from huggingface_hub import cached_download, hf_hub_url
from transformers import DPTConfig, DPTFeatureExtractor, DPTForDepthEstimation, DPTForSemanticSegmentation
from transformers.utils import logging
def get_dpt_co... | Copy/paste/tweak model's weights to our DPT structure. |
11,103 | 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 ConvNextConfig, ConvNextFeatureExtractor, ConvNextForImageClassification
from transformers.utils import logging
def get_convnext_config(checkpoint_... | Copy/paste/tweak model's weights to our ConvNext structure. |
11,104 | 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 (
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention... | 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... |
11,105 | 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 BaseModelOutputWithPast, CausalLMOutputWithPast
from ...modeling_utils import PreTrainedModel
from ...utils im... | null |
11,106 | 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 BaseModelOutputWithPast, CausalLMOutputWithPast
from ...modeling_utils import PreTrainedModel
from ...utils im... | null |
11,107 | import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
import regex as re
from ...utils import is_tf_available, is_torch_available, logging
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
The provided code snippet includ... | 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... |
11,108 | import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
import regex as re
from ...utils import is_tf_available, is_torch_available, logging
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
The provided code snippet includ... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
11,109 | import copy
import math
import random
from typing import Any, Dict, 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,
... | 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. |
11,110 | import copy
import math
import random
from typing import Any, Dict, 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,
... | Make causal mask used for bi-directional self-attention. |
11,111 | import copy
import math
import random
from typing import Any, Dict, 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,
... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
11,112 | import argparse
import torch
from torch import nn
from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification
def remove_ignore_keys_(state_dict):
ignore_keys = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.... | null |
11,113 | import random
from dataclasses import dataclass
from typing import Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
from ... | Computes the weighted average of a given tensor across a given `dim`, masking values associated with weight zero, meaning instead of `nan * 0 = nan` you will get `0 * 0 = 0`. Args: input_tensor (`torch.FloatTensor`): Input tensor, of which the average must be computed. weights (`torch.FloatTensor`, *optional*): Weights... |
11,114 | import random
from dataclasses import dataclass
from typing import Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
from ... | Make causal mask used for bi-directional self-attention. |
11,115 | import random
from dataclasses import dataclass
from typing import Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
from ... | Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. |
11,116 | import copy
import math
import warnings
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from ...activations import ACT2FN... | null |
11,117 | import copy
import math
import warnings
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from ...activations import ACT2FN... | null |
11,118 | import copy
import math
import warnings
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from ...activations import ACT2FN... | null |
11,119 | import copy
import math
import warnings
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from ...activations import ACT2FN... | Expands attention_mask from `[batch_size, seq_len]` to `[batch_size, 1, target_seq_len, source_seq_len]`. |
11,120 | import copy
import math
import warnings
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from ...activations import ACT2FN... | null |
11,121 | import copy
import math
import warnings
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from ...activations import ACT2FN... | null |
11,122 | import copy
import math
import warnings
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from ...activations import ACT2FN... | Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The predictions for each example. targets: A float tensor with the same shape as inputs. Stores the binary classification label for each element in inputs (0 for the negative class and 1 for the positive class). |
11,123 | import copy
import math
import warnings
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from ...activations import ACT2FN... | Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. Args: inputs (`torch.FloatTensor` of arbitrary shape): The predictions for each example. targets (`torch.FloatTensor` with the same shape as `inputs`) A tensor storing the binary classification label for each element in the `inputs` (0 for th... |
11,124 | import copy
import math
import warnings
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from ...activations import ACT2FN... | Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format. Returns: `torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2) |
11,125 | import copy
import math
import warnings
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from ...activations import ACT2FN... | Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format (x_0, y_0, x_1, y_1). |
11,126 | import copy
import math
import warnings
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn.functional as F
from torch import Tensor, nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from ...activations import ACT2FN... | null |
11,127 | import argparse
import json
from pathlib import Path
import torch
from PIL import Image
import requests
from huggingface_hub import cached_download, hf_hub_url
from transformers import DeformableDetrConfig, DeformableDetrFeatureExtractor, DeformableDetrForObjectDetection
from transformers.utils import logging
logger = ... | Copy/paste/tweak model's weights to our Deformable DETR structure. |
11,128 | import os
def load_cuda_kernels():
from torch.utils.cpp_extension import load
root = os.path.join(os.path.dirname(os.path.realpath(__file__)), "custom_kernel")
src_files = [
os.path.join(root, filename)
for filename in [
"vision.cpp",
os.path.join("cpu", "ms_deform_... | null |
11,129 | import pathlib
import warnings
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
from ...utils import TensorType, is_torch_ava... | Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format (x_0, y_0, x_1, y_1). |
11,130 | import pathlib
import warnings
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
from ...utils import TensorType, is_torch_ava... | Converts a NumPy array of bounding boxes of shape (number of bounding boxes, 4) of corners format (x_0, y_0, x_1, y_1) to center format (center_x, center_y, width, height). |
11,131 | import pathlib
import warnings
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
from ...utils import TensorType, is_torch_ava... | Compute the bounding boxes around the provided panoptic segmentation masks. The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions. Returns a [N, 4] tensor, with the boxes in corner (xyxy) format. |
11,132 | import pathlib
import warnings
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
from ...utils import TensorType, is_torch_ava... | null |
11,133 | import pathlib
import warnings
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from PIL import Image
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...image_utils import ImageFeatureExtractionMixin, is_torch_tensor
from ...utils import TensorType, is_torch_ava... | null |
11,134 | 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 AutoFeatureExtractor, Swinv2Config, Swinv2ForImageClassification
def get_swinv2_config(swinv2_name):
config = Swinv2Config()
na... | null |
11,135 | import collections.abc
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...model... | Partitions the given input into windows. |
11,136 | import collections.abc
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...model... | Merges windows to produce higher resolution features. |
11,137 | import collections.abc
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...model... | 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... |
11,138 | import collections.abc
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 BaseModelOutputWithNoAttention, ImageClassifierOutputWithNo... | 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... |
11,139 | import argparse
import json
from collections import OrderedDict
from pathlib import Path
import torch
from PIL import Image
import requests
from huggingface_hub import hf_hub_download
from transformers import PoolFormerConfig, PoolFormerFeatureExtractor, PoolFormerForImageClassification
from transformers.utils import l... | Copy/paste/tweak model's weights to our PoolFormer structure. |
11,140 | import importlib
import re
import warnings
from collections import OrderedDict
from typing import List, Union
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...utils import CONFIG_NAME, logging
CONFIG_MAPPING_NAMES = OrderedDict(
[
... | Converts a config class name to the corresponding model type |
11,141 | import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CO... | null |
11,142 | import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CO... | Loads the tokenizer configuration from a pretrained model tokenizer configuration. Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained model configuration hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-lev... |
11,143 | import importlib
from collections import OrderedDict
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...utils import copy_func, logging
from .configuration_auto import AutoConfig, model_type_to_module_name, replace_list_option_in_docstrings
def... | null |
11,144 | import importlib
from collections import OrderedDict
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...utils import copy_func, logging
from .configuration_auto import AutoConfig, model_type_to_module_name, replace_list_option_in_docstrings
CLAS... | null |
11,145 | import importlib
from collections import OrderedDict
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...utils import copy_func, logging
from .configuration_auto import AutoConfig, model_type_to_module_name, replace_list_option_in_docstrings
def... | null |
11,146 | import importlib
from collections import OrderedDict
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...utils import copy_func, logging
from .configuration_auto import AutoConfig, model_type_to_module_name, replace_list_option_in_docstrings
def... | null |
11,147 | import importlib
import json
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...tokenization_utils import PreTrainedTokenizer
from ...to... | null |
11,148 | import importlib
import json
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Dict, Optional, Tuple, Union
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...tokenization_utils import PreTrainedTokenizer
from ...to... | Loads the tokenizer configuration from a pretrained model tokenizer configuration. Args: pretrained_model_name_or_path (`str` or `os.PathLike`): This can be either: - a string, the *model id* of a pretrained model configuration hosted inside a model repo on huggingface.co. Valid model ids can be located at the root-lev... |
11,149 | import importlib
import inspect
import json
from collections import OrderedDict
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils import TOKENIZER_CONFIG_FILE
fro... | null |
11,150 | import numpy as np
from ...utils import logging
from ..t5.modeling_flax_t5 import FlaxT5EncoderModel, FlaxT5ForConditionalGeneration, FlaxT5Model
from .configuration_mt5 import MT5Config
The provided code snippet includes necessary dependencies for implementing the `shift_tokens_right` function. Write a Python functio... | Shift input ids one token to the right. |
11,151 | import collections
import copy
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
The provided code snippet includes necessary depen... | Loads a vocabulary file into a dictionary. |
11,152 | import collections
import copy
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
from ...utils import logging
The provided code snippet includes necessary depen... | Runs basic whitespace cleaning and splitting on a piece of text. |
11,153 | 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 YolosConfig, YolosFeatureExtractor, YolosForObjectDetection
from transformers.utils import logging
def read_in_q_k_v(state_dict: dict, config: Yol... | null |
11,154 | 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 YolosConfig, YolosFeatureExtractor, YolosForObjectDetection
from transformers.utils import logging
def get_yolos_config(yolos_name: str) -> YolosCo... | Copy/paste/tweak model's weights to our YOLOS structure. |
11,160 | import collections.abc
import math
from dataclasses import dataclass
from typing import Dict, List, Optional, Set, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...mo... | Compute the DICE loss, similar to generalized IOU for masks Args: inputs: A float tensor of arbitrary shape. The predictions for each example. targets: A float tensor with the same shape as inputs. Stores the binary classification label for each element in inputs (0 for the negative class and 1 for the positive class). |
11,161 | import collections.abc
import math
from dataclasses import dataclass
from typing import Dict, List, Optional, Set, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...mo... | Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. Args: inputs (`torch.FloatTensor` of arbitrary shape): The predictions for each example. targets (`torch.FloatTensor` with the same shape as `inputs`) A tensor storing the binary classification label for each element in the `inputs` (0 for th... |
11,162 | import collections.abc
import math
from dataclasses import dataclass
from typing import Dict, List, Optional, Set, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...mo... | Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format. Returns: `torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2) |
11,163 | import collections.abc
import math
from dataclasses import dataclass
from typing import Dict, List, Optional, Set, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from ...activations import ACT2FN
from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
from ...mo... | null |
11,164 | 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. |
11,165 | 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. |
11,166 | 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]`. |
11,169 | import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import torch
import torch.nn as nn
from torch import Tensor
from huggingface_hub import cached_download, hf_hub_download
from transformers import AutoFeatureExtr... | null |
11,170 | import math
from collections import OrderedDict
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 (
BaseModelOutputWithNoAttenti... | 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... |
11,171 | import json
import os
import random
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...file_utils import ExplicitEnum, PaddingStrategy, TensorType, add_end_docstrings, is_pandas_available
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from... | 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... |
11,172 | import json
import os
import random
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...file_utils import ExplicitEnum, PaddingStrategy, TensorType, add_end_docstrings, is_pandas_available
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from... | Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). |
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