code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Union[str, Any]:
UpperCamelCase_ = [False] * len(UpperCAmelCase_ )
UpperCamelCase_ = [-1] * len(UpperCAmelCase_ )
def dfs(UpperCamelCase_ , UpperCamelCase_ ):
UpperCamelCas... | 353 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> list:
UpperCamelCase_ = int(UpperCamelCase_ )
if n_element < 1:
UpperCamelCase_ = ValueError("a should be a positive number" )
raise my_error
UpperCamelCase_ = ... | 328 | 0 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared t... | 354 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_I... | 328 | 0 |
"""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,
)
_UpperCAmelCase = {"configuration_xglm... | 355 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_UpperCAmelCase = {'UserAgent': UserAgent().random}
def lowerCAmelCase_ ( UpperCamelCase_ ) -> dict:
UpperCamelCase_ = script.conte... | 328 | 0 |
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> Any:
_enforce_args(__UpperCAmelCase , __UpperCAmelCase )
if n == 0:
return 0
UpperCamelCase_ = float("-inf" )
for i in range(1 , n + 1 ):
... | 356 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_... | 328 | 0 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_UpperCAmelCase = pytest.mark.integration
@pytest.mark.parametrize("path" , ["paws", "csv"] ... | 357 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __in... | 328 | 0 |
"""simple docstring"""
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
f... | 358 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'la... | 328 | 0 |
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> int:
if len(_lowercase ) != len(_lowercase ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise... | 359 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
for param in module.parameters():
UpperCamelCase_ = False
def lowerCAmelCase_ ( ) -> Dict:
UpperCamelCa... | 328 | 0 |
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 transformers.utils import logging
lo... | 360 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = '▁'
_UpperCAmelCase = {'vocab_file': 'spiece.model'}
_UpperCAmelCase = ... | 328 | 0 |
import doctest
from collections import deque
import numpy as np
class _UpperCamelCase :
def __init__( self: List[Any] ) -> None:
"""simple docstring"""
UpperCamelCase_ = [2, 1, 2, -1]
UpperCamelCase_ = [1, 2, 3... | 361 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
if not i... | 328 | 0 |
"""simple docstring"""
from functools import lru_cache
@lru_cache
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
if num < 0:
raise ValueError("Number should not be negative." )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __... | 362 |
import argparse
import json
from tqdm import tqdm
def lowerCAmelCase_ ( ) -> Tuple:
UpperCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=UpperCamelCase_ , default="biencoder-nq-de... | 328 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def lowerCAmelCase_ ( UpperCamelCase_ ) -> tuple:
... | 363 |
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> str:
UpperCamelCase_ = BeautifulSoup(requests.get(UpperCamelCase_ , params=UpperCamelCase_ ).content , "html.parser" )
UpperCam... | 328 | 0 |
import argparse
import os
import re
_UpperCAmelCase = """src/transformers/models/auto"""
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_UpperCAmelCase = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict... | 364 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self: List[str] , *,
_SC... | 328 | 0 |
from __future__ import annotations
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> list[str]:
if partitions <= 0:
raise ValueError("partitions must be a positive number!" )
if partitions > number_of_bytes:
r... | 365 |
from functools import lru_cache
def lowerCAmelCase_ ( UpperCamelCase_ ) -> set:
UpperCamelCase_ = 2
UpperCamelCase_ = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 328 | 0 |
import re
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Any:
if len(re.findall("[ATCG]" , lowerCAmelCase__ ) ) != len(lowerCAmelCase__ ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" , "TAGC" ) ... | 366 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
UpperCamelCase_ = len(UpperCamelCase_ )
UpperCamelCase_ = len(matrix[0] )
UpperCamelCase_ = min(UpperCamelCase_ , UpperCamelCase_ )
for row in range(UpperCamelCase... | 328 | 0 |
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,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import ... | 367 |
import math
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> List[str]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(UpperCamelCase_ )
else:
if x == 0... | 328 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_UpperCAmelCase = {"configuration_deit": ["DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeiTConfig", "DeiTOnnxConfig"]}
try:
if ... | 368 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_UpperCAmelCase = transforms.Comp... | 328 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase = {
'configuration_mask2former': [
'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Mask2FormerConfig',
... | 369 |
import re
from filelock import FileLock
try:
import nltk
_UpperCAmelCase = True
except (ImportError, ModuleNotFoundError):
_UpperCAmelCase = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def lowerCAmelCase_ ( U... | 328 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
_UpperCAmelCase = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SPEECHT5_PRETRAINED_HIFIGAN... | 370 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_deter... | 328 | 0 |
from typing import Any
class _UpperCamelCase :
def __init__( self: Any , _SCREAMING_SNAKE_CASE: int ) -> Tuple:
"""simple docstring"""
UpperCamelCase_ = data
UpperCamelCase_ = None
class _UpperCam... | 371 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _UpperCamelCase :
def __init__( self: str ) -> Any:
"""simple docstring"""
UpperCamelCase_ = ""
UpperCamelCase_ = "... | 328 | 0 |
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> Optional[int]:
return x if y == 0 else greatest_common_divisor(__a , x % y )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> Tuple:
return (x * y) // grea... | 350 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_UpperCAmelCase = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pr... | 328 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase... | 351 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class _UpperCamelCase ( lo... | 328 | 0 |
"""simple docstring"""
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
... | 352 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokeniz... | 328 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase_ )
class _UpperCamelCase ( lowerCAmelCase_ ):
# `task` is not a ClassVar since ... | 353 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> list:
UpperCamelCase_ = int(UpperCamelCase_ )
if n_element < 1:
UpperCamelCase_ = ValueError("a should be a positive number" )
raise my_error
UpperCamelCase_ = ... | 328 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCAmelCase_ ( ) -> Optional[Any]:
UpperCamelCase_ = ArgumentParser(
description=(
"... | 354 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_I... | 328 | 0 |
"""simple docstring"""
import torch
from diffusers import StableDiffusionPipeline
_UpperCAmelCase = 'path-to-your-trained-model'
_UpperCAmelCase = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')
_UpperCAmelCase = 'A photo of sks dog in a bucket'
_U... | 355 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_UpperCAmelCase = {'UserAgent': UserAgent().random}
def lowerCAmelCase_ ( UpperCamelCase_ ) -> dict:
UpperCamelCase_ = script.conte... | 328 | 0 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class _UpperCamelCase ( nn.Module ):
_UpperCamelCase : Optional[int] = 4_2
_UpperCamelCase : Union[str, Any] = jnp.floataa
def lowercase ( self: Union[str, Any] ) ... | 356 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_... | 328 | 0 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class _UpperCamelCase ( lowerCAmelCase_ ):
def __init__( self: Optional[int] ... | 357 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __in... | 328 | 0 |
"""simple docstring"""
import os
def lowerCAmelCase_ ( ) -> Any:
UpperCamelCase_ = os.path.dirname(os.path.realpath(_A ) )
UpperCamelCase_ = os.path.join(_A , "triangle.txt" )
with open(_A ) as f:
UpperCamel... | 358 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'la... | 328 | 0 |
import math
from datetime import datetime, timedelta
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Union[str, Any]:
UpperCamelCase_ = year % 19
UpperCamelCase_ = year % 4
UpperCamelCase_ = year % 7
UpperCamelCase_ = mat... | 359 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
for param in module.parameters():
UpperCamelCase_ = False
def lowerCAmelCase_ ( ) -> Dict:
UpperCamelCa... | 328 | 0 |
import functools
from typing import Any
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> bool:
# Validation
if not isinstance(UpperCamelCase_ , UpperCamelCase_ ) or len(UpperCamelCase_ ) == 0:
raise ValueError("the string sho... | 360 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = '▁'
_UpperCAmelCase = {'vocab_file': 'spiece.model'}
_UpperCAmelCase = ... | 328 | 0 |
import string
def lowerCAmelCase_ ( UpperCamelCase_ ) -> None:
for key in range(len(string.ascii_uppercase ) ):
UpperCamelCase_ = ''''''
for symbol in message:
if symbol in string.ascii_uppercase:
... | 361 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
if not i... | 328 | 0 |
"""simple docstring"""
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorStat... | 362 |
import argparse
import json
from tqdm import tqdm
def lowerCAmelCase_ ( ) -> Tuple:
UpperCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=UpperCamelCase_ , default="biencoder-nq-de... | 328 | 0 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_UpperCAmelCase = 'sshleifer/bart-tiny-random'
_UpperCAmelCas... | 363 |
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> str:
UpperCamelCase_ = BeautifulSoup(requests.get(UpperCamelCase_ , params=UpperCamelCase_ ).content , "html.parser" )
UpperCam... | 328 | 0 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
if len(SCREAMING_SNAKE_CASE_ ) <= 1:
return [tuple(SCREAMING_SNAKE_CASE_ )]
UpperCamelCase_ = []
def generate(UpperCamelCase_ , UpperCamelCase_ ):
if k == 1:
... | 364 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self: List[str] , *,
_SC... | 328 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
if not is_torch... | 365 |
from functools import lru_cache
def lowerCAmelCase_ ( UpperCamelCase_ ) -> set:
UpperCamelCase_ = 2
UpperCamelCase_ = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 328 | 0 |
from timeit import timeit
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
if number < 0:
raise ValueError("the value of input must not be negative" )
UpperCamelCase_ = 0
while number:
number &= number - 1
res... | 366 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
UpperCamelCase_ = len(UpperCamelCase_ )
UpperCamelCase_ = len(matrix[0] )
UpperCamelCase_ = min(UpperCamelCase_ , UpperCamelCase_ )
for row in range(UpperCamelCase... | 328 | 0 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
if "model" in orig_key:
UpperCamelCase_ = orig_key.replace("model." , "" )
if "norm1" in orig_key:
... | 367 |
import math
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> List[str]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(UpperCamelCase_ )
else:
if x == 0... | 328 | 0 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class _UpperCamelCase ( unittest.TestCas... | 368 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_UpperCAmelCase = transforms.Comp... | 328 | 0 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
_UpperCAmelCase = ... | 369 |
import re
from filelock import FileLock
try:
import nltk
_UpperCAmelCase = True
except (ImportError, ModuleNotFoundError):
_UpperCAmelCase = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def lowerCAmelCase_ ( U... | 328 | 0 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( __snake_case ):
_UpperCamelCase : int = """ClapFeatureExtractor"""
_UpperCamelCase : List[str] = ("""RobertaTokenizer""... | 370 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_deter... | 328 | 0 |
import warnings
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
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'''nv... | 371 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _UpperCamelCase :
def __init__( self: str ) -> Any:
"""simple docstring"""
UpperCamelCase_ = ""
UpperCamelCase_ = "... | 328 | 0 |
import torch
from diffusers import StableDiffusionPipeline
_UpperCAmelCase = 'path-to-your-trained-model'
_UpperCAmelCase = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('cuda')
_UpperCAmelCase = 'A photo of sks dog in a bucket'
_UpperCAmelCase = pipe(p... | 350 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_UpperCAmelCase = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pr... | 328 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowerCAmelCase_ ( UpperCamelCase_ ... | 351 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class _UpperCamelCase ( lo... | 328 | 0 |
"""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/LICENSE-2.0... | 352 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokeniz... | 328 | 0 |
from math import pi
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> float:
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(9_0, 1_0))
| 353 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> list:
UpperCamelCase_ = int(UpperCamelCase_ )
if n_element < 1:
UpperCamelCase_ = ValueError("a should be a positive number" )
raise my_error
UpperCamelCase_ = ... | 328 | 0 |
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria,
M... | 354 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_I... | 328 | 0 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pi... | 355 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_UpperCAmelCase = {'UserAgent': UserAgent().random}
def lowerCAmelCase_ ( UpperCamelCase_ ) -> dict:
UpperCamelCase_ = script.conte... | 328 | 0 |
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> int:
return 1 if input_a == input_a else 0
def lowerCAmelCase_ ( ) -> None:
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0
assert xnor_gate... | 356 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_... | 328 | 0 |
import os
from datetime import datetime as dt
from github import Github
_UpperCAmelCase = [
'good first issue',
'feature request',
'wip',
]
def lowerCAmelCase_ ( ) -> Union[str, Any]:
UpperCamelCase_ = Github(os.environ["GITHUB_TOKEN"] )
Uppe... | 357 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __in... | 328 | 0 |
"""simple docstring"""
from math import factorial
def lowerCAmelCase_ ( UpperCamelCase_ = 20 ) -> int:
UpperCamelCase_ = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
UpperCamelCase_ = n // 2
... | 358 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'la... | 328 | 0 |
from __future__ import annotations
import math
def lowerCAmelCase_ ( UpperCamelCase_ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even... | 359 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
for param in module.parameters():
UpperCamelCase_ = False
def lowerCAmelCase_ ( ) -> Dict:
UpperCamelCa... | 328 | 0 |
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcesso... | 360 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = '▁'
_UpperCAmelCase = {'vocab_file': 'spiece.model'}
_UpperCAmelCase = ... | 328 | 0 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_UpperCAmelCase = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pr... | 361 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
if not i... | 328 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : Union[str, Any] = ['''image_processor''', '''tokenizer''']
_UpperCamelCase... | 362 |
import argparse
import json
from tqdm import tqdm
def lowerCAmelCase_ ( ) -> Tuple:
UpperCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=UpperCamelCase_ , default="biencoder-nq-de... | 328 | 0 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...f... | 363 |
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> str:
UpperCamelCase_ = BeautifulSoup(requests.get(UpperCamelCase_ , params=UpperCamelCase_ ).content , "html.parser" )
UpperCam... | 328 | 0 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_UpperCAmelCase = 'Usage of script: script_name <size_of_canvas:int>'
_UpperCAmelCase = [0] * 1_0_0 + [1] * 1_0
random.shuffle(choice)
def lowerCAmelCase_ ( Upper... | 364 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self: List[str] , *,
_SC... | 328 | 0 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMSched... | 365 |
from functools import lru_cache
def lowerCAmelCase_ ( UpperCamelCase_ ) -> set:
UpperCamelCase_ = 2
UpperCamelCase_ = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 328 | 0 |
from __future__ import annotations
import math
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> int:
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
... | 366 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
UpperCamelCase_ = len(UpperCamelCase_ )
UpperCamelCase_ = len(matrix[0] )
UpperCamelCase_ = min(UpperCamelCase_ , UpperCamelCase_ )
for row in range(UpperCamelCase... | 328 | 0 |
from functools import lru_cache
def lowerCAmelCase_ ( UpperCamelCase_ ) -> set:
UpperCamelCase_ = 2
UpperCamelCase_ = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 367 |
import math
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> List[str]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(UpperCamelCase_ )
else:
if x == 0... | 328 | 0 |
def lowerCAmelCase_ ( UpperCamelCase_ = 1000 ) -> int:
UpperCamelCase_ = 3
UpperCamelCase_ = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
... | 368 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_UpperCAmelCase = transforms.Comp... | 328 | 0 |
"""simple docstring"""
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_UpperCAmelCase = {
'facebook/mask2former-swin-small-coco-instance': (
'https://huggingface.co/face... | 369 |
import re
from filelock import FileLock
try:
import nltk
_UpperCAmelCase = True
except (ImportError, ModuleNotFoundError):
_UpperCAmelCase = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def lowerCAmelCase_ ( U... | 328 | 0 |
def lowerCAmelCase_ ( UpperCamelCase_ = "The quick brown fox jumps over the lazy dog" , ):
UpperCamelCase_ = set()
# Replace all the whitespace in our sentence
UpperCamelCase_ = input_str.replace(" " , "" )
for alpha in input_str:
... | 370 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_deter... | 328 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : str = ['''image_processor''', '''tokenizer''']
_UpperCamelCase : List[str] ... | 371 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _UpperCamelCase :
def __init__( self: str ) -> Any:
"""simple docstring"""
UpperCamelCase_ = ""
UpperCamelCase_ = "... | 328 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.... | 350 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_UpperCAmelCase = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pr... | 328 | 0 |
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
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils imp... | 351 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class _UpperCamelCase ( lo... | 328 | 0 |
"""simple docstring"""
from __future__ import annotations
_UpperCAmelCase = '#'
class _UpperCamelCase :
def __init__( self: Dict ) -> None:
"""simple docstring"""
UpperCamelCase_ = {}
def lowercase ( self: Option... | 352 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokeniz... | 328 | 0 |
from math import pi, sqrt
def lowerCAmelCase_ ( UpperCamelCase_ ) -> float:
if num <= 0:
raise ValueError("math domain error" )
if num > 171.5:
raise OverflowError("math range error" )
elif num - int(UpperCamelCase_ ) not in (0... | 353 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> list:
UpperCamelCase_ = int(UpperCamelCase_ )
if n_element < 1:
UpperCamelCase_ = ValueError("a should be a positive number" )
raise my_error
UpperCamelCase_ = ... | 328 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : List[Any] = ['''image_processor''', '''tokenizer''']
_UpperCamelCase : Optional[... | 354 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_I... | 328 | 0 |
"""simple docstring"""
import re
from filelock import FileLock
try:
import nltk
_UpperCAmelCase = True
except (ImportError, ModuleNotFoundError):
_UpperCAmelCase = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def... | 355 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_UpperCAmelCase = {'UserAgent': UserAgent().random}
def lowerCAmelCase_ ( UpperCamelCase_ ) -> dict:
UpperCamelCase_ = script.conte... | 328 | 0 |
import pytest
_UpperCAmelCase = '__dummy_dataset1__'
_UpperCAmelCase = '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikian... | 356 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_... | 328 | 0 |
_UpperCAmelCase = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
UpperCamelCase_ = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
... | 357 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __in... | 328 | 0 |
"""simple docstring"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_UpperCAmelCase = {'UserAgent': UserAgent().random}
def lowerCAmelCase_ ( UpperCamelCase_ ) -> dict:
UpperCamelCase... | 358 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'la... | 328 | 0 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks,... | 359 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
for param in module.parameters():
UpperCamelCase_ = False
def lowerCAmelCase_ ( ) -> Dict:
UpperCamelCa... | 328 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, t... | 360 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = '▁'
_UpperCAmelCase = {'vocab_file': 'spiece.model'}
_UpperCAmelCase = ... | 328 | 0 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
UpperCamelCase_ = len(UpperCamelCase_ )
UpperCamelCase_ = len(matrix[0] )
UpperCamelCase_ = min(UpperCamelCase_ , UpperCamelCase_ )
for row in range(UpperCamelCase... | 361 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
if not i... | 328 | 0 |
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
_UpperCAmelCase = 'scheduler_config.json'
class _Upp... | 362 |
import argparse
import json
from tqdm import tqdm
def lowerCAmelCase_ ( ) -> Tuple:
UpperCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=UpperCamelCase_ , default="biencoder-nq-de... | 328 | 0 |
from ..utils import DummyObject, requires_backends
class _UpperCamelCase ( metaclass=lowerCAmelCase_ ):
_UpperCamelCase : Optional[Any] = ['''flax''', '''transformers''']
def __init__( self: int , *_SCREAMING_SNAKE_CASE: Tuple , **_SCREAMING_SNAK... | 363 |
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> str:
UpperCamelCase_ = BeautifulSoup(requests.get(UpperCamelCase_ , params=UpperCamelCase_ ).content , "html.parser" )
UpperCam... | 328 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
... | 364 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self: List[str] , *,
_SC... | 328 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase_ )
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase ... | 365 |
from functools import lru_cache
def lowerCAmelCase_ ( UpperCamelCase_ ) -> set:
UpperCamelCase_ = 2
UpperCamelCase_ = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 328 | 0 |
# Copyright 2023 The HuggingFace Inc. 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... | 366 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
UpperCamelCase_ = len(UpperCamelCase_ )
UpperCamelCase_ = len(matrix[0] )
UpperCamelCase_ = min(UpperCamelCase_ , UpperCamelCase_ )
for row in range(UpperCamelCase... | 328 | 0 |
# using dfs for finding eulerian path traversal
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=None ) -> List[Any]:
UpperCamelCase_ = (path or []) + [u]
for v in graph[u]:
if visi... | 367 |
import math
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> List[str]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(UpperCamelCase_ )
else:
if x == 0... | 328 | 0 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
UpperCamelCase_ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def lowerCAmelCase_ ( UpperCamelCase_ = 100 ) -> int:
UpperCamelCa... | 368 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_UpperCAmelCase = transforms.Comp... | 328 | 0 |
"""simple docstring"""
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqd... | 369 |
import re
from filelock import FileLock
try:
import nltk
_UpperCAmelCase = True
except (ImportError, ModuleNotFoundError):
_UpperCAmelCase = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def lowerCAmelCase_ ( U... | 328 | 0 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHea... | 370 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_deter... | 328 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.mod... | 371 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class _UpperCamelCase :
def __init__( self: str ) -> Any:
"""simple docstring"""
UpperCamelCase_ = ""
UpperCamelCase_ = "... | 328 | 0 |
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMix... | 350 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_UpperCAmelCase = '\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pr... | 328 | 0 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Tuple:
return getitem, k
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> str... | 351 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class _UpperCamelCase ( lo... | 328 | 0 |
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
_UpperCAmelCase = 0
_UpperCAmelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
... | 352 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokeniz... | 328 | 0 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_UpperCAmelCase = transforms.Comp... | 353 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> list:
UpperCamelCase_ = int(UpperCamelCase_ )
if n_element < 1:
UpperCamelCase_ = ValueError("a should be a positive number" )
raise my_error
UpperCamelCase_ = ... | 328 | 0 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('--dump_path', default=None, type=str, requi... | 354 |
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import TEXT_GUIDED_I... | 328 | 0 |
"""simple docstring"""
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, ... | 355 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_UpperCAmelCase = {'UserAgent': UserAgent().random}
def lowerCAmelCase_ ( UpperCamelCase_ ) -> dict:
UpperCamelCase_ = script.conte... | 328 | 0 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_UpperCAmelCase = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_UpperCAmelCase = [ord(letter) for letter in string.ascii_lowercase]
_UpperCAmelCase ... | 356 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_... | 328 | 0 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_com... | 357 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __in... | 328 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extrac... | 358 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_UpperCAmelCase = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
'text-classification',
'la... | 328 | 0 |
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
UpperCamelCase_ = hex_num.strip()
if not hex_num:
raise ValueError("No value was passed to the function" )
UpperCamelCase_ = hex_num[0] == "-"
if is_negative:
Up... | 359 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def lowerCAmelCase_ ( UpperCamelCase_ ) -> int:
for param in module.parameters():
UpperCamelCase_ = False
def lowerCAmelCase_ ( ) -> Dict:
UpperCamelCa... | 328 | 0 |
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> int:
return abs(UpperCamelCase_ ) if a == 0 else greatest_common_divisor(b % a , UpperCamelCase_ )
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> int:
... | 360 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = '▁'
_UpperCAmelCase = {'vocab_file': 'spiece.model'}
_UpperCAmelCase = ... | 328 | 0 |
# flake8: noqa
# Lint as: python3
_UpperCAmelCase = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, i... | 361 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_UpperCAmelCase = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasTokenizer'],
}
try:
if not i... | 328 | 0 |
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_... | 362 |
import argparse
import json
from tqdm import tqdm
def lowerCAmelCase_ ( ) -> Tuple:
UpperCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--src_path" , type=UpperCamelCase_ , default="biencoder-nq-de... | 328 | 0 |
import math
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> List[str]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(UpperCamelCase_ )
else:
if x == 0... | 363 |
import requests
from bsa import BeautifulSoup
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ ) -> str:
UpperCamelCase_ = BeautifulSoup(requests.get(UpperCamelCase_ , params=UpperCamelCase_ ).content , "html.parser" )
UpperCam... | 328 | 0 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
f... | 364 |
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ):
@register_to_config
def __init__( self: List[str] , *,
_SC... | 328 | 0 |
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