code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from typing import List
import numpy as np
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = {key: len(_lowerCamelCase ) for key, value in gen_kwargs.items() if isinstance(_lowerCamelCase , _lowerCame... | 718 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"facebook/data2vec-vision-base-ft... | 658 | 0 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCAmelCase_ ( a , ... | 719 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
_snake_case = {
"cola": 2,
"m... | 658 | 0 |
def A ( _lowerCamelCase ):
'''simple docstring'''
if number > 0:
raise ValueError("input must be a negative integer" )
_lowerCAmelCase : int = len(bin(_lowerCamelCase )[3:] )
_lowerCAmelCase... | 720 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_snake_case = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evalua... | 658 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_snake_case = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDepe... | 721 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def A ( _lowerCamelCase , _lowerCamelCase=False ):
'''simple docstring'''
_lowerCAmelCase : Dict = OmegaConf.load(_lowerC... | 658 | 0 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"nielsr/canine-s": 2048,
}
# Unicode defines 1,114,112 total “codepoints”
_snake_case = 111_4112
... | 700 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
class UpperCAmelCase_ ( a):
l... | 658 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = "▁"
_snake_case = {"vocab... | 701 |
from __future__ import annotations
def A ( _lowerCamelCase ):
'''simple docstring'''
if not nums:
raise ValueError("List is empty" )
return sum(_lowerCamelCase ) / len(_lowerCamelCase )
if __name__ == "__main__":
import ... | 658 | 0 |
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
_snake_case = logging.get_logger(__name__)
def A ( _lowerCamelCase , _l... | 702 |
def A ( _lowerCamelCase ):
'''simple docstring'''
if length <= 0 or not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(_lowerCamelCase... | 658 | 0 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse("3.8"):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
_snake_case = ""
if v... | 703 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def A ( _lowerCamelCase ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def ... | 658 | 0 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCAmelCase_ ( a , a):
@register_to_config
def __init__( self, *,
__a = 4, __a = 768, __a, __a, ):
... | 704 |
import requests
from bsa import BeautifulSoup
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = BeautifulSoup(requests.get(_lowerCamelCase , params=_lowerCamelCase ).content ... | 658 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase_ ( a):
... | 705 |
def A ( _lowerCamelCase = 1_000_000 ):
'''simple docstring'''
_lowerCAmelCase : Any = 1
_lowerCAmelCase : Optional[Any] = 1
_lowerCAmelCase : List[str] = {1: 1}
for inputa in range(2 , ... | 658 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 100 , ):
'''simple docstring'''
_lowerCAmelCase : ... | 706 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
_snake_case = "https://openaipublic.azureedg... | 658 | 0 |
import heapq
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : list[list] = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a ... | 707 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple... | 658 | 0 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetr... | 708 |
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = len(_lowerCamelCase )
for i in range(1 , _lowerCamelCase ):
_lowerCAmelCase : List[Any] = collection[i]
... | 658 | 0 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = 0
if start < end:
... | 709 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsof... | 658 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
_snake_case = {
"configuration_speecht5": [
"SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SPEECHT5_PRETRAIN... | 710 |
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
def count_of_possible_combinations(_lowerCamelCase ) -> int:
if target < 0:
return 0
if target == 0:
... | 658 | 0 |
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( a):
lowerCamelCase__ = (CMStochasticIterativeScheduler,)
lowerCamelCase__ = 10
def snake_case__ ( self, ... | 711 |
import string
def A ( _lowerCamelCase ):
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
_lowerCAmelCase : str = ""
for symbol in message:
if symbol in string.asc... | 658 | 0 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class UpperCAmelCase_ :
def __init__( self, __a, __a, __a, __a, __a, __a=0.2, __a=0.2):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = ... | 712 |
import requests
from bsa import BeautifulSoup
def A ( _lowerCamelCase = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
_lowerCAmelCase : str = BeautifulSoup(requests.get(_lowerCamelCase ).text , "html.parser"... | 658 | 0 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def A ( _lowerCamelCase , _lowerC... | 713 |
from __future__ import annotations
from collections.abc import MutableSequence
class UpperCAmelCase_ :
def __init__( self, __a, __a):
'''simple docstring'''
if len(__a) != degree + 1:
raise ValueError(
... | 658 | 0 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
_snake_case = {
"text_branch": "text_model",
"audio_branch": "audio_model.audio_encoder",
"attn": "attention.self",
"self.proj": "output.dense",
"att... | 714 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-cased": "https://huggingface.co/xlnet... | 658 | 0 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
_snake_case = "https://openaipublic.azureedg... | 715 |
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f'''{price_plus_tax(100, 0.25) = }''')
print(f'''{price_plus_tax(125.50, 0.05) = }''')
| 658 | 0 |
def A ( _lowerCamelCase ):
'''simple docstring'''
if length <= 0 or not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(_lowerCamelCase... | 716 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
_snake_case = logging.get_logger(__name__)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'upernet'
def __init__( se... | 658 | 0 |
import heapq as hq
import math
from collections.abc import Iterator
class UpperCAmelCase_ :
def __init__( self, __a):
'''simple docstring'''
_lowerCAmelCase : Dict = str(id_)
_lowerCAmelCase : List[str] =... | 717 |
import baseaa
def A ( _lowerCamelCase ):
'''simple docstring'''
return baseaa.aaaencode(string.encode("utf-8" ) )
def A ( _lowerCamelCase ):
'''simple docstring'''
return baseaa.aaadecode(_lowerCamelCas... | 658 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
),
# See all Vivit model... | 718 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"facebook/data2vec-vision-base-ft... | 658 | 0 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize("repo_id" , ["canonical_dataset_name", "org-name/dataset-name"] )
@pytest.mark.parametrize("path" , ["filename.csv", "filename with blanks.csv"] )
@pytest.mark.parametr... | 719 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
_snake_case = {
"cola": 2,
"m... | 658 | 0 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
_snake_case = logging.get_logger(__name__)
class UpperCAmelCase_ ( a):
def __init__( self, *__a, **__a):
'''simp... | 720 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_snake_case = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evalua... | 658 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
_snake_case = {
"facebook/convnextv2-tiny-1k-224": "https://hug... | 721 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def A ( _lowerCamelCase , _lowerCamelCase=False ):
'''simple docstring'''
_lowerCAmelCase : Dict = OmegaConf.load(_lowerC... | 658 | 0 |
from __future__ import annotations
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ):
'''simple docstring'''
_lowerCAmelCase : List[str] = len(_lowerCamelC... | 700 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
class UpperCAmelCase_ ( a):
l... | 658 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_snake_case = TypeVar("T")
class UpperCAmelCase_ ( Generic[T]):
def __init__( self, __a):
'''simple docstring'''
_lowerCAmelCase : ... | 701 |
from __future__ import annotations
def A ( _lowerCamelCase ):
'''simple docstring'''
if not nums:
raise ValueError("List is empty" )
return sum(_lowerCamelCase ) / len(_lowerCamelCase )
if __name__ == "__main__":
import ... | 658 | 0 |
from collections import defaultdict
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = first_str.lower().strip()
_lowerCAmelCase : Union[str, Any] = second_str.lower().... | 702 |
def A ( _lowerCamelCase ):
'''simple docstring'''
if length <= 0 or not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(_lowerCamelCase... | 658 | 0 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def A ( *_lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase=True , _lowerCamelCase=2 ):
'''simple docstring'''
from .. im... | 703 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def A ( _lowerCamelCase ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def ... | 658 | 0 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoC... | 704 |
import requests
from bsa import BeautifulSoup
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = BeautifulSoup(requests.get(_lowerCamelCase , params=_lowerCamelCase ).content ... | 658 | 0 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF... | 705 |
def A ( _lowerCamelCase = 1_000_000 ):
'''simple docstring'''
_lowerCAmelCase : Any = 1
_lowerCAmelCase : Optional[Any] = 1
_lowerCAmelCase : List[str] = {1: 1}
for inputa in range(2 , ... | 658 | 0 |
'''simple docstring'''
from __future__ import annotations
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : list[list[int]] = []
_lowerCAmelCase : list[int] = []
... | 706 |
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
_snake_case = "https://openaipublic.azureedg... | 658 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}
try:
i... | 707 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple... | 658 | 0 |
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_snake_case = 10
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _l... | 708 |
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = len(_lowerCamelCase )
for i in range(1 , _lowerCamelCase ):
_lowerCAmelCase : List[Any] = collection[i]
... | 658 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
_snake_case = logging.get_logger(__name__)
_snake_case = {"vocab_fil... | 709 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/focalnet-tiny": "https://huggingface.co/microsof... | 658 | 0 |
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
_lowerCAmelCase : ... | 710 |
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
def count_of_possible_combinations(_lowerCamelCase ) -> int:
if target < 0:
return 0
if target == 0:
... | 658 | 0 |
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = len(_lowerCamelCase )
for i in range(1 , _lowerCamelCase ):
_lowerCAmelCase : List[Any] = collection[i]
... | 711 |
import string
def A ( _lowerCamelCase ):
'''simple docstring'''
for key in range(len(string.ascii_uppercase ) ):
_lowerCAmelCase : str = ""
for symbol in message:
if symbol in string.asc... | 658 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 712 |
import requests
from bsa import BeautifulSoup
def A ( _lowerCamelCase = "https://www.worldometers.info/coronavirus" ):
'''simple docstring'''
_lowerCAmelCase : str = BeautifulSoup(requests.get(_lowerCamelCase ).text , "html.parser"... | 658 | 0 |
from __future__ import annotations
def A ( _lowerCamelCase ):
'''simple docstring'''
if len(_lowerCamelCase ) == 0:
return []
_lowerCAmelCase : List[Any] = min(_lowerCamelCase ), max(_lowerCamelCase )
_l... | 713 |
from __future__ import annotations
from collections.abc import MutableSequence
class UpperCAmelCase_ :
def __init__( self, __a, __a):
'''simple docstring'''
if len(__a) != degree + 1:
raise ValueError(
... | 658 | 0 |
from __future__ import annotations
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if b == 0:
return (1, 0)
(_lowerCAmelCase) : Any = extended_euclid(_lowerCamelCase , a % b )
... | 714 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
"xlnet-large-cased": "https://huggingface.co/xlnet... | 658 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
Seg... | 715 |
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f'''{price_plus_tax(100, 0.25) = }''')
print(f'''{price_plus_tax(125.50, 0.05) = }''')
| 658 | 0 |
from __future__ import annotations
from random import random
class UpperCAmelCase_ :
def __init__( self, __a = None):
'''simple docstring'''
_lowerCAmelCase : Tuple = value
_lowerCAmelCase : Dict = r... | 716 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
_snake_case = logging.get_logger(__name__)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'upernet'
def __init__( se... | 658 | 0 |
def A ( _lowerCamelCase = 10 , _lowerCamelCase = 22 ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = range(1 , _lowerCamelCase )
_lowerCAmelCase : Optional[int] = range(1 , _lowerC... | 717 |
import baseaa
def A ( _lowerCamelCase ):
'''simple docstring'''
return baseaa.aaaencode(string.encode("utf-8" ) )
def A ( _lowerCamelCase ):
'''simple docstring'''
return baseaa.aaadecode(_lowerCamelCas... | 658 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_snake_case = {"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNo... | 718 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"facebook/data2vec-vision-base-ft... | 658 | 0 |
from __future__ import annotations
from typing import TypedDict
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 42
lowerCamelCase__ = 42
def A ( _lowerCamelCase ):
'''simple docstring'''
if not isinstance(_lowerCamelCase ... | 719 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
_snake_case = {
"cola": 2,
"m... | 658 | 0 |
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructu... | 720 |
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
_snake_case = "\\n@INPROCEEDINGS{Papineni02bleu:a,\n author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei-jing Zhu},\n title = {BLEU: a Method for Automatic Evalua... | 658 | 0 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class UpperCAmelCase_ ( e... | 721 |
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def A ( _lowerCamelCase , _lowerCamelCase=False ):
'''simple docstring'''
_lowerCAmelCase : Dict = OmegaConf.load(_lowerC... | 658 | 0 |
"""simple docstring"""
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_snake_case = (
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH AH QH",
"TS KS... | 659 |
"""simple docstring"""
def snake_case ( _a: int , _a: list[int] , _a: int )-> int:
'''simple docstring'''
def count_of_possible_combinations(_a: int ) -> int:
if target < 0:
return 0
if target == 0:
... | 659 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from ... | 659 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfi... | 659 | 1 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_c... | 659 |
"""simple docstring"""
def snake_case ( _a: list[list[float]] )-> list[list[float]]:
'''simple docstring'''
lowerCamelCase__ = []
for data in source_data:
for i, el in enumerate(_a ):
if len(_a ) < i + 1:
... | 659 | 1 |
"""simple docstring"""
def snake_case ( _a: int )-> int:
'''simple docstring'''
lowerCamelCase__ = abs(_a )
lowerCamelCase__ = 0
while n > 0:
res += n % 10
n //= 10
return res
def snake_... | 659 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def snake_case ( _a: int , _a: int = 2 , _a: int = 1 , _a: int = 3 , )-> int | None:
'''simple docstring'''
if num < 2:
raise ValueError('The input va... | 659 | 1 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def snake_case ( _a: str ... | 659 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Optio... | 659 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
... | 659 |
"""simple docstring"""
from __future__ import annotations
_snake_case = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def snake_case ( _a: list[list[int]] , _a: list[int] , _a: list[int] , _a: int , _a:... | 659 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _a ( SCREAMING_SNAK... | 659 |
"""simple docstring"""
def snake_case ( _a: int = 4000000 )-> int:
'''simple docstring'''
lowerCamelCase__ = [0, 1]
lowerCamelCase__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2]... | 659 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
_snake_case = list[list[float | int]]
def snake_case ( _a: Matrix , _a: Matrix )-> Matrix:
'''simple docstring'''
lowerCamelCase__ ... | 659 |
"""simple docstring"""
def snake_case ( _a: List[Any] , _a: Any , _a: str , _a: List[Any] )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = [False] * len(_a )
lowerCamelCase__ = []
queue.append(_a ... | 659 | 1 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V ... | 659 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torc... | 659 | 1 |
"""simple docstring"""
from collections.abc import Generator
from math import sin
def snake_case ( _a: bytes )-> bytes:
'''simple docstring'''
if len(_a ) != 32:
raise ValueError('Input must be of length 32' )
lowerCamelCase__ ... | 659 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def snake_case ( )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' ,... | 659 | 1 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class _a :
def __init__( self : List[str] ... | 659 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"configuration_blenderbot":... | 659 | 1 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from tr... | 659 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-p... | 659 | 1 |
"""simple docstring"""
def snake_case ( _a: int = 4000000 )-> int:
'''simple docstring'''
lowerCamelCase__ = [0, 1]
lowerCamelCase__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2]... | 659 |
"""simple docstring"""
def snake_case ( _a: int )-> int:
'''simple docstring'''
lowerCamelCase__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def snake_case ( _a: int )-> int:
'... | 659 | 1 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def snake_case ( _a: list[Any] )-> None:
'''simple docstring'''
create_state_space_tree(_a , [] , 0 )
def snake_case ( _a: list[Any] , _a: ... | 659 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterator... | 659 | 1 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
_snake_case = logging.getLogger(__name__)
_sn... | 659 |
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_snake_case = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def sna... | 659 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_av... | 659 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...sched... | 659 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
... | 659 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_av... | 659 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig",
... | 659 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from... | 659 | 1 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def snake_case ( _a: int = 8 )-> str:
'''simple docstring'''
lowerCamelCase__ = ascii_letters + d... | 659 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 659 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespac... | 659 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_snake_case = logging.get_lo... | 659 | 1 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
... | 659 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V ... | 659 | 1 |
"""simple docstring"""
def snake_case ( _a: list[list[float]] )-> list[list[float]]:
'''simple docstring'''
lowerCamelCase__ = []
for data in source_data:
for i, el in enumerate(_a ):
if len(_a ) < i + 1:
... | 659 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_snake_case = TypeVar("KEY")
_snake_case = TypeVar("VAL")
@dataclass(frozen=SCREAMING_SNAKE_CASE_ , slots=SCREAMING_SN... | 659 | 1 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def snake_case ( _a: str = "" )-> dict[str, float]:
'''simple docstring'''
lowerCamelCase__ = url or 'https://www.imdb.com/chart/t... | 659 |
"""simple docstring"""
def snake_case ( _a: int , _a: list[int] , _a: int )-> int:
'''simple docstring'''
def count_of_possible_combinations(_a: int ) -> int:
if target < 0:
return 0
if target == 0:
... | 659 | 1 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(... | 659 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfi... | 659 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin... | 659 |
"""simple docstring"""
def snake_case ( _a: list[list[float]] )-> list[list[float]]:
'''simple docstring'''
lowerCamelCase__ = []
for data in source_data:
for i, el in enumerate(_a ):
if len(_a ) < i + 1:
... | 659 | 1 |
"""simple docstring"""
from manim import *
class _a ( SCREAMING_SNAKE_CASE_ ):
def _UpperCamelCase ( self : Optional[int] ):
lowerCamelCase__ = Rectangle(height=0.5 , width=0.5 )
lowerCamelCase__ = Rectan... | 659 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def snake_case ( _a: int , _a: int = 2 , _a: int = 1 , _a: int = 3 , )-> int | None:
'''simple docstring'''
if num < 2:
raise ValueError('The input va... | 659 | 1 |
"""simple docstring"""
from collections.abc import Sequence
def snake_case ( _a: Sequence[int] | None = None )-> int:
'''simple docstring'''
if nums is None or not nums:
raise ValueError('Input sequence should not be empty' )
lowerCame... | 659 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Optio... | 659 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
_snake_case = 0
_snake_case = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0,... | 659 |
"""simple docstring"""
from __future__ import annotations
_snake_case = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def snake_case ( _a: list[list[int]] , _a: list[int] , _a: list[int] , _a: int , _a:... | 659 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | 659 |
"""simple docstring"""
def snake_case ( _a: int = 4000000 )-> int:
'''simple docstring'''
lowerCamelCase__ = [0, 1]
lowerCamelCase__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2]... | 659 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
... | 659 |
"""simple docstring"""
def snake_case ( _a: List[Any] , _a: Any , _a: str , _a: List[Any] )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = [False] * len(_a )
lowerCamelCase__ = []
queue.append(_a ... | 659 | 1 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
_snake_case = logging.get_logger(__name__)
def snake_case ( _a: Union[str, Any] , _a: List[Any] ... | 659 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torc... | 659 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {... | 659 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def snake_case ( )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' ,... | 659 | 1 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWi... | 659 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"configuration_blenderbot":... | 659 | 1 |
"""simple docstring"""
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
... | 659 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/swinv2-tiny-patch4-window8-256": (
"https://huggingface.co/microsoft/swinv2-tiny-p... | 659 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | 659 |
"""simple docstring"""
def snake_case ( _a: int )-> int:
'''simple docstring'''
lowerCamelCase__ = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def snake_case ( _a: int )-> int:
'... | 659 | 1 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from tran... | 659 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterator... | 659 | 1 |
"""simple docstring"""
_snake_case = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_snake_case = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
_snake_case = {
0: "Sunday",
1: "Monday",
2: "Tuesday",
3: "Wednesday",
4: "Thursday",
5: "Friday",
6:... | 659 |
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_snake_case = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def sna... | 659 | 1 |
"""simple docstring"""
def snake_case ( _a: int )-> "list[int]":
'''simple docstring'''
if upper_limit < 0:
raise ValueError('Limit for the Catalan sequence must be ≥ 0' )
lowerCamelCase__ = [0] * (upper_limit + 1)
# Base ... | 659 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...sched... | 659 | 1 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _a ( SCREAMING_SN... | 659 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_av... | 659 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class _a :
a_ : List[str]
a_ : Opt... | 659 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from... | 659 | 1 |
"""simple docstring"""
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def snake_case ( _a: List[str] , _a: Dict=() , _a: List[Any]=None ,... | 659 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 659 | 1 |
"""simple docstring"""
from collections import deque
def snake_case ( _a: Union[str, Any] )-> Optional[int]:
'''simple docstring'''
lowerCamelCase__ = len(_a )
lowerCamelCase__ = deque()
lowerCamelCase__ = [Fal... | 659 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_snake_case = logging.get_lo... | 659 | 1 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
_snake_case = "\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n jour... | 659 |
"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V ... | 659 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .va... | 659 |
"""simple docstring"""
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_snake_case = TypeVar("KEY")
_snake_case = TypeVar("VAL")
@dataclass(frozen=SCREAMING_SNAKE_CASE_ , slots=SCREAMING_SN... | 659 | 1 |
"""simple docstring"""
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import ... | 659 |
"""simple docstring"""
def snake_case ( _a: int , _a: list[int] , _a: int )-> int:
'''simple docstring'''
def count_of_possible_combinations(_a: int ) -> int:
if target < 0:
return 0
if target == 0:
... | 659 | 1 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
_snake_case = "__DUMMY_TRANSFORMERS_USER__"
_snake_case = "Dummy User"
_snake_case ... | 659 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_snake_case = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "M2M100OnnxConfi... | 659 | 1 |
"""simple docstring"""
from numpy import exp, pi, sqrt
def snake_case ( _a: Tuple , _a: float = 0.0 , _a: float = 1.0 )-> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if _... | 659 |
"""simple docstring"""
def snake_case ( _a: list[list[float]] )-> list[list[float]]:
'''simple docstring'''
lowerCamelCase__ = []
for data in source_data:
for i, el in enumerate(_a ):
if len(_a ) < i + 1:
... | 659 | 1 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accel... | 659 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def snake_case ( _a: int , _a: int = 2 , _a: int = 1 , _a: int = 3 , )-> int | None:
'''simple docstring'''
if num < 2:
raise ValueError('The input va... | 659 | 1 |
"""simple docstring"""
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
_snake_case = {
"E": 12.70,
"T": 9.06,
"A": 8.17,
"O": 7.51,
"I": 6.97,
"N": 6.75,
"S": 6.33,
"H": 6.09,
"R": 5.99,
"D": 4.25,
"... | 659 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Optio... | 659 | 1 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Attention-Network/van... | 659 |
"""simple docstring"""
from __future__ import annotations
_snake_case = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def snake_case ( _a: list[list[int]] , _a: list[int] , _a: list[int] , _a: int , _a:... | 659 | 1 |
"""simple docstring"""
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_snake_case = logging.get_... | 659 |
"""simple docstring"""
def snake_case ( _a: int = 4000000 )-> int:
'''simple docstring'''
lowerCamelCase__ = [0, 1]
lowerCamelCase__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2]... | 659 | 1 |
"""simple docstring"""
_snake_case = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
... | 659 |
"""simple docstring"""
def snake_case ( _a: List[Any] , _a: Any , _a: str , _a: List[Any] )-> List[Any]:
'''simple docstring'''
lowerCamelCase__ = [False] * len(_a )
lowerCamelCase__ = []
queue.append(_a ... | 659 | 1 |
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